Malicious actors, specially trained professionals operating anonymously on the dark web (DW) platform to conduct cyber fraud, illegal drug supply, online kidnapping orders, CryptoLocker induction, contract hacking, terrorist recruitment portals on the online social network (OSN) platform, and financing are always a possibility in the hyperspace. The amount and variety of unlawful actions are increasing, which has prompted law enforcement (LE) agencies to develop efficient prevention tactics. In the current atmosphere of rapidly expanding cybercrime, conventional crime-solving methods are unable to produce results due to their slowness and inefficiency. The methods for accurately predicting crime before it happens "automated machine" to help police officers ease the burden on personnel while also assisting in preventing offense. To achieve and explain the results of a few cases in which such approaches were applied, we advise combining machine learning (ML) with computer vision (CV) strategies. This study's objective is to present dark web crime statistics and a forecasting model for generating alerts of illegal operations like drug supply, people smuggling, terrorist staffing and radicalization, and deceitful activities that are connected to gangs or organizations showing online presence using ML and CV to help law enforcement organizations identify, and accumulate proactive tactics for solving crimes.
Survey on Crime Interpretation and Forecasting Using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to analyze crime data and predict crime patterns in Bangalore, India. It first provides background on the increasing issue of crime and importance of understanding crime patterns. It then reviews related work applying clustering, classification, and other algorithms to crime data from various locations. Next, it discusses motivations for using machine learning to predict crimes in advance. The paper then compares different studies that have used techniques like k-means clustering, decision trees, naive bayes, and random forests on crime data. It evaluates these techniques and their limitations in accurately analyzing crime patterns and predicting future crimes. Finally, the document proposes using these machine learning methods and data mining approaches on crime data from Bangalore to help law enforcement agencies
Physical and Cyber Crime Detection using Digital Forensic Approach: A Complet...IJARIIT
Criminalization may be a general development that has significantly extended in previous few years. In
order, to create the activity of the work businesses easy, use of technology is important. Crime investigation analysis
is a section records in data mining plays a crucial role in terms of predicting and learning the criminals. In our
paper, we've got planned an incorporated version for physical crime as well as cybercrime analysis. Our approach
uses data mining techniques for crime detection and criminal identity for physical crimes and digitized forensic tools
(DFT) for evaluating cybercrimes. The presented tool named as Comparative Digital Forensic Process tool
(CDFPT) is entirely based on digital forensic model and its stages named as Comparative Digital Forensic Process
Model (CDFPM). The primary step includes accepting the case details, categorizing the crime case as physical crime
or cybercrime and sooner or later storing the data in particular databases. For physical crime analysis we've used kmeans
approach cluster set of rules to make crime clusters. The k-means method effects are a lot advantageous by the
utilization of GMAPI generation. This provides advanced and consumer-friendly visual-aid to k-means approach for
tracing the region of the crime. we have applied KNN for criminal identification with the
help of observing beyond crimes and finding similar ones that suit this crime, if no past document is discovered then
the new crime sample are introduced to the crime data-set. With the advancements of web, the network form has
become much more complicated and attacking methods are further more than that as well. For crime analysis
we're detecting the attacks executed on host system through an outsider the usage of
assorted digitized forensic tools to produce information security with the help of generating reports for an
event which could need any investigation. Our digitized technique aids the development of the society
by helping the investigation businesses to follow a custom-built investigative technique in crime analysis and criminal
identification as opposed to manually looking the database to analyze criminal activities, and as a
result facilitate them in combating crimes.
Online Attack Types of Data Breach and Cyberattack Prevention MethodsBRNSSPublicationHubI
This document summarizes research on online attack types and cyberattack prevention methods. It discusses how phishing attacks are commonly used by cybercriminals to steal private information by tricking users. The document then reviews several related works on detecting different cyber threat types using data analysis techniques like machine learning. It surveys recent methods for strengthening the identification of phishing websites using algorithms like oppositional cuckoo search and fuzzy logic classification.
Applications Of Artificial Intelligence Techniques To Combating Cyber Crimes ...Audrey Britton
This document summarizes research on applying artificial intelligence techniques to combat cyber crimes. It discusses how AI methods like neural networks, genetic algorithms, artificial immune systems, and intelligent agents have been used for intrusion detection and prevention. The document provides examples of existing applications, such as neural network systems for detecting distributed denial-of-service attacks, spam filtering, and identifying zombie computers. It also outlines desired characteristics for effective intrusion detection and prevention systems.
Enhancements in the world of digital forensicsIAESIJAI
Currently, the rapid advancement of computer systems and mobile phones has resulted in their utilization in unlawful acts. Ensuring adequate and effective security measures poses a difficult task due to the intricate nature of these devices, thereby exacerbating the challenges associated with investigating crimes involving them. Digital forensics, which involves investigating cyber crimes, plays a crucial role in this realm. Extensive research has been conducted in this field to aid forensic investigations in addressing contemporary obstacles. This paper aims to explore the progress made in the applications of digital forensics and security, encompassing various aspects, and provide insights into the evolution of digital forensics over the past five years.
Applications of artificial intelligence techniques to combating cyber crimes ...ijaia
This document discusses applications of artificial intelligence techniques for combating cyber crimes. It provides an overview of how AI, including techniques like neural networks, intelligent agents, artificial immune systems, and machine learning, can help detect and prevent cyber attacks. Specifically, the document reviews research applying these AI methods to intrusion detection and prevention systems. It examines desired characteristics for effective intrusion detection and outlines some examples of existing work using artificial neural networks and intelligent agents for applications like denial of service detection, malware classification, and spam filtering.
Classification of Malware Attacks Using Machine Learning In Decision TreeCSCJournals
Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
Survey on Crime Interpretation and Forecasting Using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to analyze crime data and predict crime patterns in Bangalore, India. It first provides background on the increasing issue of crime and importance of understanding crime patterns. It then reviews related work applying clustering, classification, and other algorithms to crime data from various locations. Next, it discusses motivations for using machine learning to predict crimes in advance. The paper then compares different studies that have used techniques like k-means clustering, decision trees, naive bayes, and random forests on crime data. It evaluates these techniques and their limitations in accurately analyzing crime patterns and predicting future crimes. Finally, the document proposes using these machine learning methods and data mining approaches on crime data from Bangalore to help law enforcement agencies
Physical and Cyber Crime Detection using Digital Forensic Approach: A Complet...IJARIIT
Criminalization may be a general development that has significantly extended in previous few years. In
order, to create the activity of the work businesses easy, use of technology is important. Crime investigation analysis
is a section records in data mining plays a crucial role in terms of predicting and learning the criminals. In our
paper, we've got planned an incorporated version for physical crime as well as cybercrime analysis. Our approach
uses data mining techniques for crime detection and criminal identity for physical crimes and digitized forensic tools
(DFT) for evaluating cybercrimes. The presented tool named as Comparative Digital Forensic Process tool
(CDFPT) is entirely based on digital forensic model and its stages named as Comparative Digital Forensic Process
Model (CDFPM). The primary step includes accepting the case details, categorizing the crime case as physical crime
or cybercrime and sooner or later storing the data in particular databases. For physical crime analysis we've used kmeans
approach cluster set of rules to make crime clusters. The k-means method effects are a lot advantageous by the
utilization of GMAPI generation. This provides advanced and consumer-friendly visual-aid to k-means approach for
tracing the region of the crime. we have applied KNN for criminal identification with the
help of observing beyond crimes and finding similar ones that suit this crime, if no past document is discovered then
the new crime sample are introduced to the crime data-set. With the advancements of web, the network form has
become much more complicated and attacking methods are further more than that as well. For crime analysis
we're detecting the attacks executed on host system through an outsider the usage of
assorted digitized forensic tools to produce information security with the help of generating reports for an
event which could need any investigation. Our digitized technique aids the development of the society
by helping the investigation businesses to follow a custom-built investigative technique in crime analysis and criminal
identification as opposed to manually looking the database to analyze criminal activities, and as a
result facilitate them in combating crimes.
Online Attack Types of Data Breach and Cyberattack Prevention MethodsBRNSSPublicationHubI
This document summarizes research on online attack types and cyberattack prevention methods. It discusses how phishing attacks are commonly used by cybercriminals to steal private information by tricking users. The document then reviews several related works on detecting different cyber threat types using data analysis techniques like machine learning. It surveys recent methods for strengthening the identification of phishing websites using algorithms like oppositional cuckoo search and fuzzy logic classification.
Applications Of Artificial Intelligence Techniques To Combating Cyber Crimes ...Audrey Britton
This document summarizes research on applying artificial intelligence techniques to combat cyber crimes. It discusses how AI methods like neural networks, genetic algorithms, artificial immune systems, and intelligent agents have been used for intrusion detection and prevention. The document provides examples of existing applications, such as neural network systems for detecting distributed denial-of-service attacks, spam filtering, and identifying zombie computers. It also outlines desired characteristics for effective intrusion detection and prevention systems.
Enhancements in the world of digital forensicsIAESIJAI
Currently, the rapid advancement of computer systems and mobile phones has resulted in their utilization in unlawful acts. Ensuring adequate and effective security measures poses a difficult task due to the intricate nature of these devices, thereby exacerbating the challenges associated with investigating crimes involving them. Digital forensics, which involves investigating cyber crimes, plays a crucial role in this realm. Extensive research has been conducted in this field to aid forensic investigations in addressing contemporary obstacles. This paper aims to explore the progress made in the applications of digital forensics and security, encompassing various aspects, and provide insights into the evolution of digital forensics over the past five years.
Applications of artificial intelligence techniques to combating cyber crimes ...ijaia
This document discusses applications of artificial intelligence techniques for combating cyber crimes. It provides an overview of how AI, including techniques like neural networks, intelligent agents, artificial immune systems, and machine learning, can help detect and prevent cyber attacks. Specifically, the document reviews research applying these AI methods to intrusion detection and prevention systems. It examines desired characteristics for effective intrusion detection and outlines some examples of existing work using artificial neural networks and intelligent agents for applications like denial of service detection, malware classification, and spam filtering.
Classification of Malware Attacks Using Machine Learning In Decision TreeCSCJournals
Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
Computer technology has provided the criminal justice system wit.docxrichardnorman90310
Computer technology has provided the criminal justice system with a number of benefits such as program algorithms that identify scanned fingerprints and facial recognition. It has increased intelligence and record keeping capabilities. However, it has provided literally millions of potential victims of crime. It started out with financial crimes (such as fraud and ransom software), and it progressed to luring victims to locations for murder, rape, and kidnapping. Victims of computer crimes include the criminal using social media as a weapon.
The commander of a newly formed cyber-crime unit is very knowledgeable about fraud and various financially motivated viruses. The one thing he is not that familiar with is the impact that social media has on victims of crimes such as cyber bullying and cyber stalking.
The commander has you temporarily attached from the human trafficking and sex crimes unit to bring his cyber teams up to date on how to understand what cyber victims of stalking and bullying are going through and how best to approach them for maximum effective investigation.
Devise a plan on explaining the aspects of cyber bullying and cyber stalking to the cyber-crime unit.
Focus your discussion on the following:
Identify three social media websites and explain how they are used to carry out cyber stalking and cyber bullying crimes.
Explain why comments made on social media are so impactful on the emotions of the victim.
Identify the types of cyber-crimes that may ultimately lead to a physical crime against the victim
Advise the cyber-crime unit team members on what types of assurances should they provide the victim to obtain his or her cooperation in the investigation and to ultimately keep him or her safe.
.
The paper emphasizes the human aspects of cyber incidents concerning protecting information and
technology assets by addressing behavioral analytics in cybersecurity for digital forensics applications.
The paper demonstrates the human vulnerabilities associated with information systems technologies and
components. This assessment is based on past literature assessments done in this area. This study also
includes analyses of various frameworks that have led to the adoption of behavioral analysis in digital
forensics. The study's findings indicate that behavioral evidence analysis should be included as part of the
digital forensics examination. The provision of standardized investigation methods and the inclusion of
human factors such as motives and behavioral tendencies are some of the factors attached to the use of
behavioral digital forensic frameworks. However, the study also appreciates the need for a more
generalizable digital forensic method.
The paper emphasizes the human aspects of cyber incidents concerning protecting information and
technology assets by addressing behavioral analytics in cybersecurity for digital forensics applications.
The paper demonstrates the human vulnerabilities associated with information systems technologies and
components. This assessment is based on past literature assessments done in this area. This study also
includes analyses of various frameworks that have led to the adoption of behavioral analysis in digital
forensics. The study's findings indicate that behavioral evidence analysis should be included as part of the
digital forensics examination. The provision of standardized investigation methods and the inclusion of
human factors such as motives and behavioral tendencies are some of the factors attached to the use of
behavioral digital forensic frameworks. However, the study also appreciates the need for a more
generalizable digital forensic method.
The paper emphasizes the human aspects of cyber incidents concerning protecting information and
technology assets by addressing behavioral analytics in cybersecurity for digital forensics applications.
The paper demonstrates the human vulnerabilities associated with information systems technologies and
components. This assessment is based on past literature assessments done in this area. This study also
includes analyses of various frameworks that have led to the adoption of behavioral analysis in digital
forensics. The study's findings indicate that behavioral evidence analysis should be included as part of the
digital forensics examination. The provision of standardized investigation methods and the inclusion of
human factors such as motives and behavioral tendencies are some of the factors attached to the use of
behavioral digital forensic frameworks. However, the study also appreciates the need for a more
generalizable digital forensic method.
Empowering Cyber Threat Intelligence with AIIJCI JOURNAL
Cyber Threat Intelligence (CTI) is gaining importance due to the rise in cyber attacks and crimes. It aims to increase administrators understanding of events and threats by gathering intelligence about criminal operations. However, there is a lack of literature on how AI algorithms can improve CTI automation. This research aims to understand CTI's importance and automate the CTI process, prioritizing important threats and providing recommendations for mitigation. The study reviews literature on AI algorithms with CTI to identify the best models and algorithms for improving automation. It also helps organizations understand and analyze data to reveal trends and patterns, providing in-depth understanding of threats. This research is suitable for entities with large datasets of intelligent information and sensitive data types.
This document provides an overview of the Internet Organised Crime Threat Assessment (IOCTA) for 2015. Some key findings include:
- Cybercrime is becoming more aggressive and confrontational, often using extortion techniques that require little technical skill. This suggests changes in offender profiles.
- Malware such as ransomware and banking Trojans remain significant threats for both individuals and businesses.
- Data breaches led to a large amount of stolen data being used for payment fraud and identity theft in 2014. However, better cooperation between law enforcement and the private sector is helping to address cybercrime challenges.
This document proposes a web-based location-aware system architecture to combat electoral crimes in Nigeria. It would allow the Independent National Electoral Commission (INEC), police, and public to exchange information about crimes in real-time. The system uses a client-server model, with GPS sensors on electoral devices to track their location if stolen. The public could anonymously report crimes or missing devices via a mobile app. INEC could then locate stolen devices on a server map. This is proposed to improve communication between authorities and the public to better detect and respond to electoral crimes.
IoT Network Attack Detection using Supervised Machine LearningCSCJournals
The use of supervised learning algorithms to detect malicious traffic can be valuable in designing intrusion detection systems and ascertaining security risks. The Internet of things (IoT) refers to the billions of physical, electronic devices around the world that are often connected over the Internet. The growth of IoT systems comes at the risk of network attacks such as denial of service (DoS) and spoofing. In this research, we perform various supervised feature selection methods and employ three classifiers on IoT network data. The classifiers predict with high accuracy if the network traffic against the IoT device was malicious or benign. We compare the feature selection methods to arrive at the best that can be used for network intrusion prediction.
Digital Evidence Analysing Industry in India.pdfGeeta Adhikari
With the rise in digital threats and cybercrimes, India Digital Forensic Market makes successive changes like integration of Artificial Intelligence, and marking its overall growth.
Computer technology has provided the criminal justice system with a .docxzollyjenkins
Computer technology has provided the criminal justice system with a number of benefits such as program algorithms that identify scanned fingerprints and facial recognition. It has increased intelligence and record keeping capabilities. However, it has provided literally millions of potential victims of crime. It started out with financial crimes (such as fraud and ransom software), and it progressed to coaxing victims to locations for murder, rape, and kidnapping. Victims of computer crimes include the criminal using social media as a weapon.
The commander of a newly formed cyber-crime unit is very knowledgeable about fraud and various financially motivated viruses. The one thing he is not that familiar with is the impact that social media has on victims of crimes such as cyber bullying and cyber stalking.
The commander has you temporarily attached from the human trafficking and sex crimes unit to bring his cyber teams up to date on how to understand what cyber victims of stalking and bullying are going through and how best to approach them for maximum effective investigation.
Devise a plan on explaining the aspects of cyber bullying and cyber stalking to the cyber-crime unit.
Focus your discussion on the following:
Explain why comments made on social media are so impactful on the emotions of the victim.
Identify the types of cyber-crimes that may ultimately lead to a physical crime against the victim
Advise the cyber-crime unit team members on what types of assurances should they provide the victim to obtain his or her cooperation in the investigation and to ultimately keep him or her safe.
.
Invesitigation of Malware and Forensic Tools on Internet IJECEIAES
Malware is an application that is harmful to your forensic information. Basically, malware analyses is the process of analysing the behaviours of malicious code and then create signatures to detect and defend against it.Malware, such as Trojan horse, Worms and Spyware severely threatens the forensic security. This research observed that although malware and its variants may vary a lot from content signatures, they share some behaviour features at a higher level which are more precise in revealing the real intent of malware. This paper investigates the various techniques of malware behaviour extraction and analysis. In addition, we discuss the implications of malware analysis tools for malware detection based on various techniques.
System call frequency analysis-based generative adversarial network model for...IJECEIAES
In today's digital age, mobile applications have become essential in connecting people from diverse domains. They play a crucial role in enabling communication, facilitating business transactions, and providing access to a range of services. Mobile communication is widespread due to its portability and ease of use, with an increasing number of mobile devices projected to reach 18.22 billion by the end of 2025. However, this convenience comes at a cost, as cybercriminals are constantly looking for ways to exploit security vulnerabilities in mobile applications. Among the several varieties of malicious applications, zero-day malware is particularly dangerous since it cannot be removed by antivirus software. To detect zeroday Android malware, this paper introduces a novel approach based on generative adversarial networks (GANs), which generates new frequencies of feature vectors from system calls. In the proposed approach, the generator is fed with a mixture of real samples and noise, and then trained to create new samples, while the discriminator model aims to classify these samples as either real or fake. We assess the performance of our model through different measures, including loss functions, the Frechet Inception distance, and the inception score evaluation metrics.
Exploratory Data Analysis and Feature Selection for Social Media Hackers Pred...CSEIJJournal
In machine learning, the intelligence of a developed model is greatly influenced by the dataset used for the
target domain on which the developed model will be deployed. Social media platform has experienced
more of hackers’ attacks on the platform in recent time. To identify a hacker on the platform, there are two
possible ways. The first is to use the activities of the user while the second is to use the supplied details the
user registered the account with. To adequately identify a social media user as hacker proactively, there
are relevant user details called features that can be used to determine whether a social media user is a
hacker or not. In this paper, an exploratory data analysis was carried out to determine the best features
that can be used by a predictive model to proactively identify hackers on the social media platform. A web
crawler was developed to mine the user dataset on which exploratory data analysis was carried out to
select the best features for the dataset which could be used to correctly identify a hacker on a social media
platform.
EXPLORATORY DATA ANALYSIS AND FEATURE SELECTION FOR SOCIAL MEDIA HACKERS PRED...CSEIJJournal
In machine learning, the intelligence of a developed model is greatly influenced by the dataset used for the
target domain on which the developed model will be deployed. Social media platform has experienced
more of hackers’ attacks on the platform in recent time. To identify a hacker on the platform, there are two
possible ways. The first is to use the activities of the user while the second is to use the supplied details the
user registered the account with. To adequately identify a social media user as hacker proactively, there
are relevant user details called features that can be used to determine whether a social media user is a
hacker or not. In this paper, an exploratory data analysis was carried out to determine the best features
that can be used by a predictive model to proactively identify hackers on the social media platform. A web
crawler was developed to mine the user dataset on which exploratory data analysis was carried out to
select the best features for the dataset which could be used to correctly identify a hacker on a social media
platform.
The proposed system aims to create a web application that allows different stakeholders involved in crime - including citizens, law enforcement officials, and non-governmental organizations - to access and analyze crime data in India. The system would allow citizens to easily report crimes online, law enforcement to track complaints and monitor predicted crime hotspots, and NGOs to utilize crime visualizations and predictions to improve their rehabilitation programs. By bringing these groups together on a single platform and applying machine learning techniques, the system seeks to help reduce crime rates in India.
This document presents a study on crime prediction and analysis using machine learning algorithms. The authors used a crime dataset from Indore, India containing timestamps, crime types, latitude and longitude. They performed data preprocessing, then trained and tested K-nearest neighbor, random forest and decision tree models on the data. The random forest model achieved the highest accuracy. Visualizations including feature selection plots, crime density graphs and heatmaps provided insights into patterns in the crime data. The authors concluded machine learning can help law enforcement predict and solve crimes faster, potentially reducing crime rates.
IMPROVE SECURITY IN SMART CITIES BASED ON IOT, SOLVE CYBER ELECTRONIC ATTACKS...IJNSA Journal
Smart cities are expected to significantly improve people's quality of life, promote sustainable development, and enhance the efficiency of operations. With the implementation of many smart devices, c problems have become a serious challenge that needs strong treatments, especially the cyber-attack, which most countries suffer from it.
My study focuses on the security of smart city systems, which include equipment like air conditioning, alarm systems, lighting, and doors. Some of the difficulties that arise daily may be found in the garage. This research aims to come up with a simulation of smart devices that can be and reduce cyber attach. Use of Cisco Packet tracer Features Simulated smart home and c devices are monitored. Simulation results show that smart objects can be connected to the home portal and objects can be successfullymonitored which leads to the idea of real-life implementation and see. In my research make manysolutions for attachingissues,which was great, and apply some wirelessprotocol.
ISSN 2395-650X
The "International Journal of Life Sciences Biotechnology and Pharma Sciences journal appears to be a valuable resource for those interested in staying updated on the latest developments and research in these important scientific fields of Life and science journal.
Botnet detection using ensemble classifiers of network flow IJECEIAES
This document summarizes a research paper that proposes using ensemble classifier algorithms to detect botnet traffic from normal network traffic. The paper experiments with bagging, boosting, and random forest classifiers to compare their ability to accurately classify network flows as either botnet or normal traffic. The models are trained and evaluated using the CTU-13 dataset, which contains labeled botnet and normal traffic data. Feature selection is performed to identify the most important attributes for classification, finding source IP, destination IP, start time, duration, protocol, protocol state, number of packets, and total bytes to be the top features. 10-fold cross validation is used to evaluate the performance of the proposed botnet detection models.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
Computer technology has provided the criminal justice system wit.docxrichardnorman90310
Computer technology has provided the criminal justice system with a number of benefits such as program algorithms that identify scanned fingerprints and facial recognition. It has increased intelligence and record keeping capabilities. However, it has provided literally millions of potential victims of crime. It started out with financial crimes (such as fraud and ransom software), and it progressed to luring victims to locations for murder, rape, and kidnapping. Victims of computer crimes include the criminal using social media as a weapon.
The commander of a newly formed cyber-crime unit is very knowledgeable about fraud and various financially motivated viruses. The one thing he is not that familiar with is the impact that social media has on victims of crimes such as cyber bullying and cyber stalking.
The commander has you temporarily attached from the human trafficking and sex crimes unit to bring his cyber teams up to date on how to understand what cyber victims of stalking and bullying are going through and how best to approach them for maximum effective investigation.
Devise a plan on explaining the aspects of cyber bullying and cyber stalking to the cyber-crime unit.
Focus your discussion on the following:
Identify three social media websites and explain how they are used to carry out cyber stalking and cyber bullying crimes.
Explain why comments made on social media are so impactful on the emotions of the victim.
Identify the types of cyber-crimes that may ultimately lead to a physical crime against the victim
Advise the cyber-crime unit team members on what types of assurances should they provide the victim to obtain his or her cooperation in the investigation and to ultimately keep him or her safe.
.
The paper emphasizes the human aspects of cyber incidents concerning protecting information and
technology assets by addressing behavioral analytics in cybersecurity for digital forensics applications.
The paper demonstrates the human vulnerabilities associated with information systems technologies and
components. This assessment is based on past literature assessments done in this area. This study also
includes analyses of various frameworks that have led to the adoption of behavioral analysis in digital
forensics. The study's findings indicate that behavioral evidence analysis should be included as part of the
digital forensics examination. The provision of standardized investigation methods and the inclusion of
human factors such as motives and behavioral tendencies are some of the factors attached to the use of
behavioral digital forensic frameworks. However, the study also appreciates the need for a more
generalizable digital forensic method.
The paper emphasizes the human aspects of cyber incidents concerning protecting information and
technology assets by addressing behavioral analytics in cybersecurity for digital forensics applications.
The paper demonstrates the human vulnerabilities associated with information systems technologies and
components. This assessment is based on past literature assessments done in this area. This study also
includes analyses of various frameworks that have led to the adoption of behavioral analysis in digital
forensics. The study's findings indicate that behavioral evidence analysis should be included as part of the
digital forensics examination. The provision of standardized investigation methods and the inclusion of
human factors such as motives and behavioral tendencies are some of the factors attached to the use of
behavioral digital forensic frameworks. However, the study also appreciates the need for a more
generalizable digital forensic method.
The paper emphasizes the human aspects of cyber incidents concerning protecting information and
technology assets by addressing behavioral analytics in cybersecurity for digital forensics applications.
The paper demonstrates the human vulnerabilities associated with information systems technologies and
components. This assessment is based on past literature assessments done in this area. This study also
includes analyses of various frameworks that have led to the adoption of behavioral analysis in digital
forensics. The study's findings indicate that behavioral evidence analysis should be included as part of the
digital forensics examination. The provision of standardized investigation methods and the inclusion of
human factors such as motives and behavioral tendencies are some of the factors attached to the use of
behavioral digital forensic frameworks. However, the study also appreciates the need for a more
generalizable digital forensic method.
Empowering Cyber Threat Intelligence with AIIJCI JOURNAL
Cyber Threat Intelligence (CTI) is gaining importance due to the rise in cyber attacks and crimes. It aims to increase administrators understanding of events and threats by gathering intelligence about criminal operations. However, there is a lack of literature on how AI algorithms can improve CTI automation. This research aims to understand CTI's importance and automate the CTI process, prioritizing important threats and providing recommendations for mitigation. The study reviews literature on AI algorithms with CTI to identify the best models and algorithms for improving automation. It also helps organizations understand and analyze data to reveal trends and patterns, providing in-depth understanding of threats. This research is suitable for entities with large datasets of intelligent information and sensitive data types.
This document provides an overview of the Internet Organised Crime Threat Assessment (IOCTA) for 2015. Some key findings include:
- Cybercrime is becoming more aggressive and confrontational, often using extortion techniques that require little technical skill. This suggests changes in offender profiles.
- Malware such as ransomware and banking Trojans remain significant threats for both individuals and businesses.
- Data breaches led to a large amount of stolen data being used for payment fraud and identity theft in 2014. However, better cooperation between law enforcement and the private sector is helping to address cybercrime challenges.
This document proposes a web-based location-aware system architecture to combat electoral crimes in Nigeria. It would allow the Independent National Electoral Commission (INEC), police, and public to exchange information about crimes in real-time. The system uses a client-server model, with GPS sensors on electoral devices to track their location if stolen. The public could anonymously report crimes or missing devices via a mobile app. INEC could then locate stolen devices on a server map. This is proposed to improve communication between authorities and the public to better detect and respond to electoral crimes.
IoT Network Attack Detection using Supervised Machine LearningCSCJournals
The use of supervised learning algorithms to detect malicious traffic can be valuable in designing intrusion detection systems and ascertaining security risks. The Internet of things (IoT) refers to the billions of physical, electronic devices around the world that are often connected over the Internet. The growth of IoT systems comes at the risk of network attacks such as denial of service (DoS) and spoofing. In this research, we perform various supervised feature selection methods and employ three classifiers on IoT network data. The classifiers predict with high accuracy if the network traffic against the IoT device was malicious or benign. We compare the feature selection methods to arrive at the best that can be used for network intrusion prediction.
Digital Evidence Analysing Industry in India.pdfGeeta Adhikari
With the rise in digital threats and cybercrimes, India Digital Forensic Market makes successive changes like integration of Artificial Intelligence, and marking its overall growth.
Computer technology has provided the criminal justice system with a .docxzollyjenkins
Computer technology has provided the criminal justice system with a number of benefits such as program algorithms that identify scanned fingerprints and facial recognition. It has increased intelligence and record keeping capabilities. However, it has provided literally millions of potential victims of crime. It started out with financial crimes (such as fraud and ransom software), and it progressed to coaxing victims to locations for murder, rape, and kidnapping. Victims of computer crimes include the criminal using social media as a weapon.
The commander of a newly formed cyber-crime unit is very knowledgeable about fraud and various financially motivated viruses. The one thing he is not that familiar with is the impact that social media has on victims of crimes such as cyber bullying and cyber stalking.
The commander has you temporarily attached from the human trafficking and sex crimes unit to bring his cyber teams up to date on how to understand what cyber victims of stalking and bullying are going through and how best to approach them for maximum effective investigation.
Devise a plan on explaining the aspects of cyber bullying and cyber stalking to the cyber-crime unit.
Focus your discussion on the following:
Explain why comments made on social media are so impactful on the emotions of the victim.
Identify the types of cyber-crimes that may ultimately lead to a physical crime against the victim
Advise the cyber-crime unit team members on what types of assurances should they provide the victim to obtain his or her cooperation in the investigation and to ultimately keep him or her safe.
.
Invesitigation of Malware and Forensic Tools on Internet IJECEIAES
Malware is an application that is harmful to your forensic information. Basically, malware analyses is the process of analysing the behaviours of malicious code and then create signatures to detect and defend against it.Malware, such as Trojan horse, Worms and Spyware severely threatens the forensic security. This research observed that although malware and its variants may vary a lot from content signatures, they share some behaviour features at a higher level which are more precise in revealing the real intent of malware. This paper investigates the various techniques of malware behaviour extraction and analysis. In addition, we discuss the implications of malware analysis tools for malware detection based on various techniques.
System call frequency analysis-based generative adversarial network model for...IJECEIAES
In today's digital age, mobile applications have become essential in connecting people from diverse domains. They play a crucial role in enabling communication, facilitating business transactions, and providing access to a range of services. Mobile communication is widespread due to its portability and ease of use, with an increasing number of mobile devices projected to reach 18.22 billion by the end of 2025. However, this convenience comes at a cost, as cybercriminals are constantly looking for ways to exploit security vulnerabilities in mobile applications. Among the several varieties of malicious applications, zero-day malware is particularly dangerous since it cannot be removed by antivirus software. To detect zeroday Android malware, this paper introduces a novel approach based on generative adversarial networks (GANs), which generates new frequencies of feature vectors from system calls. In the proposed approach, the generator is fed with a mixture of real samples and noise, and then trained to create new samples, while the discriminator model aims to classify these samples as either real or fake. We assess the performance of our model through different measures, including loss functions, the Frechet Inception distance, and the inception score evaluation metrics.
Exploratory Data Analysis and Feature Selection for Social Media Hackers Pred...CSEIJJournal
In machine learning, the intelligence of a developed model is greatly influenced by the dataset used for the
target domain on which the developed model will be deployed. Social media platform has experienced
more of hackers’ attacks on the platform in recent time. To identify a hacker on the platform, there are two
possible ways. The first is to use the activities of the user while the second is to use the supplied details the
user registered the account with. To adequately identify a social media user as hacker proactively, there
are relevant user details called features that can be used to determine whether a social media user is a
hacker or not. In this paper, an exploratory data analysis was carried out to determine the best features
that can be used by a predictive model to proactively identify hackers on the social media platform. A web
crawler was developed to mine the user dataset on which exploratory data analysis was carried out to
select the best features for the dataset which could be used to correctly identify a hacker on a social media
platform.
EXPLORATORY DATA ANALYSIS AND FEATURE SELECTION FOR SOCIAL MEDIA HACKERS PRED...CSEIJJournal
In machine learning, the intelligence of a developed model is greatly influenced by the dataset used for the
target domain on which the developed model will be deployed. Social media platform has experienced
more of hackers’ attacks on the platform in recent time. To identify a hacker on the platform, there are two
possible ways. The first is to use the activities of the user while the second is to use the supplied details the
user registered the account with. To adequately identify a social media user as hacker proactively, there
are relevant user details called features that can be used to determine whether a social media user is a
hacker or not. In this paper, an exploratory data analysis was carried out to determine the best features
that can be used by a predictive model to proactively identify hackers on the social media platform. A web
crawler was developed to mine the user dataset on which exploratory data analysis was carried out to
select the best features for the dataset which could be used to correctly identify a hacker on a social media
platform.
The proposed system aims to create a web application that allows different stakeholders involved in crime - including citizens, law enforcement officials, and non-governmental organizations - to access and analyze crime data in India. The system would allow citizens to easily report crimes online, law enforcement to track complaints and monitor predicted crime hotspots, and NGOs to utilize crime visualizations and predictions to improve their rehabilitation programs. By bringing these groups together on a single platform and applying machine learning techniques, the system seeks to help reduce crime rates in India.
This document presents a study on crime prediction and analysis using machine learning algorithms. The authors used a crime dataset from Indore, India containing timestamps, crime types, latitude and longitude. They performed data preprocessing, then trained and tested K-nearest neighbor, random forest and decision tree models on the data. The random forest model achieved the highest accuracy. Visualizations including feature selection plots, crime density graphs and heatmaps provided insights into patterns in the crime data. The authors concluded machine learning can help law enforcement predict and solve crimes faster, potentially reducing crime rates.
IMPROVE SECURITY IN SMART CITIES BASED ON IOT, SOLVE CYBER ELECTRONIC ATTACKS...IJNSA Journal
Smart cities are expected to significantly improve people's quality of life, promote sustainable development, and enhance the efficiency of operations. With the implementation of many smart devices, c problems have become a serious challenge that needs strong treatments, especially the cyber-attack, which most countries suffer from it.
My study focuses on the security of smart city systems, which include equipment like air conditioning, alarm systems, lighting, and doors. Some of the difficulties that arise daily may be found in the garage. This research aims to come up with a simulation of smart devices that can be and reduce cyber attach. Use of Cisco Packet tracer Features Simulated smart home and c devices are monitored. Simulation results show that smart objects can be connected to the home portal and objects can be successfullymonitored which leads to the idea of real-life implementation and see. In my research make manysolutions for attachingissues,which was great, and apply some wirelessprotocol.
ISSN 2395-650X
The "International Journal of Life Sciences Biotechnology and Pharma Sciences journal appears to be a valuable resource for those interested in staying updated on the latest developments and research in these important scientific fields of Life and science journal.
Botnet detection using ensemble classifiers of network flow IJECEIAES
This document summarizes a research paper that proposes using ensemble classifier algorithms to detect botnet traffic from normal network traffic. The paper experiments with bagging, boosting, and random forest classifiers to compare their ability to accurately classify network flows as either botnet or normal traffic. The models are trained and evaluated using the CTU-13 dataset, which contains labeled botnet and normal traffic data. Feature selection is performed to identify the most important attributes for classification, finding source IP, destination IP, start time, duration, protocol, protocol state, number of packets, and total bytes to be the top features. 10-fold cross validation is used to evaluate the performance of the proposed botnet detection models.
Similar to Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
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gray level transformation unit 3(image processing))
Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 5, October 2023, pp. 5354~5365
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5354-5365 5354
Journal homepage: http://ijece.iaescore.com
Techniques for predicting dark web events focused on the
delivery of illicit products and ordered crime
Romil Rawat1
, Olukayode Ayodele Oki2
, Sakthidasan Sankaran3
, Hector Florez4
,
Sunday Adeola Ajagbe5
1
Department of Computer and Information Technology, University of Extremadura, Badajoz, Spain
2
Department of Information Technology, Walter Sisulu University, East London, South Africa
3
Department of Electronics and Communication Engineering, Hindustan Institute of Technology and Science, Chennai, India
4
ITI Research Group, Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia
5
Department of Computer and Industrial Production Engineering, First Technical University Ibadan, Ibadan, Nigeria
Article Info ABSTRACT
Article history:
Received Aug 27, 2022
Revised Mar 10, 2023
Accepted Mar 12, 2023
Malicious actors, specially trained professionals operating anonymously on
the dark web (DW) platform to conduct cyber fraud, illegal drug supply,
online kidnapping orders, CryptoLocker induction, contract hacking, terrorist
recruitment portals on the online social network (OSN) platform, and
financing are always a possibility in the hyperspace. The amount and variety
of unlawful actions are increasing, which has prompted law enforcement (LE)
agencies to develop efficient prevention tactics. In the current atmosphere of
rapidly expanding cybercrime, conventional crime-solving methods are
unable to produce results due to their slowness and inefficiency. The methods
for accurately predicting crime before it happens "automated machine" to help
police officers ease the burden on personnel while also assisting in preventing
offense. To achieve and explain the results of a few cases in which such
approaches were applied, we advise combining machine learning (ML) with
computer vision (CV) strategies. This study's objective is to present dark web
crime statistics and a forecasting model for generating alerts of illegal
operations like drug supply, people smuggling, terrorist staffing and
radicalization, and deceitful activities that are connected to gangs or
organizations showing online presence using ML and CV to help law
enforcement organizations identify, and accumulate proactive tactics for
solving crimes.
Keywords:
Computer vision
Crime prediction
Cyber terrorism
Darkweb
Information security
Machine learning
This is an open access article under the CC BY-SA license.
Corresponding Author:
Sunday Adeola Ajagbe
Department of Computer and Industrial Production Engineering, First Technical University Ibadan
Km 15 Lagos–Ibadan Expy, 200255, Ibadan, Oyo, Nigeria
Email: sunday.ajagbe@tech-u.edu.ng
1. INTRODUCTION
In the field of artificial intelligence (AI), computer vision (CV) and image processing frameworks are
used to identify and interpret the visual world, giving the machine a sense of awareness of its virtual cognitive
surroundings [1]–[3]. The modeling of actual criminal patterns and signature loops is made easier by CV [4],
[5]. By obtaining three-dimensional (3D) visuals in object detection, face and gesture recognition, image
computation, criminal image identification, terrorist location and weapons recognition, illicit activity
monitoring and alarming, geolocation tagging, and suspicious word scripts, mathematical approaches have
been developed to retrieve and make it possible for automated processes (AS) to interpret data [6], [7]. VLFeat
is a tool that can produce results much more quickly than anticipated [8], [9]. VLFeat was defined as a library
of as a library of CV algorithms in an artificial intelligence-machine learning (AI-ML) study that was utilized
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to carry out fast prototyping and identify the human posture using face detection and human identification [10].
A computer system may learn from past events despite needing to be expressly programmed using the machine
learning (ML) approach [11] and ML understands the precise architecture and frameworks [12], [13]. Although
the nature of various offenses and their motives often appear to be random, ML may aid with pattern
identification [14], [15] and content modelling utilizing natural language processing (NLP) techniques based
on CV.
Mahanolob is a cybercrime analysis and prediction tool with a dynamic time-wrapping technique that
enables both the forecast of crime and the eventual perpetrator's apprehension, according to related research
[1]. Furthermore, the law enforcement (LE) in the United States, United Kingdom, and other European nations
use crime-predicting apps to monitor criminal activity on social media and in specific geographic areas [16].
National authorities and the government now encourage the merging of ML techniques with technological
automated systems and criminal intelligence [17]. It provides the means of a brand-new, strong machine
(a group of programs) to aid in the pursuit of criminal investigations. The main objective of crime prediction
is to foresee incidents before they take place so that a prior plan may be developed in recognized terrorist and
criminal hotspots, which helps to comprehend terrorist action plans. Forecasting, policing with a high degree
of precision, government critical resources such as police manpower educated with cyber tools based on ML,
detectives, and financial specialists at cyber network usage, to battle crime.
Figure 1 outlines the background behaviors of illicit activities containing terrorist cyber events,
triggering modes, propagation modes, damaging factors, and structure of losses. Cyber vulnerabilities are
planned and created by terrorists in a sequential manner, identifying the effects on online platforms. The cyber
threat always triggers an evaluation of distributed factors. The purposes of triggering are to make the post-
global and attract supporters to join terrorist camps using the online social networks (OSN) platform.
The remainder of the section is laid out as follows: section 2 discusses terrorism diagnosis using social
media. Section 3 discusses crime anticipation using ML techniques. Section 4 discusses crime prediction
approaches CV, ML, deep learning (DL). Section 5 discusses proposed concept and design for cybercrime
prediction with crime statistics. Section 6 provides the results and discussion of this research while. And
section 7 concludes the paper with future work. Contribution: i) to show crime prediction using ML, CV, and
DL with crime statistics for tracing illicit events channels and criminals’ associations; ii) to show the hidden
criminal market business tracing; and iii) to help the law enforcement officials to trace criminal events on
digital platforms, so that action can be taken.
Figure 1. Terrorist cyber events triggering
2. TERRORISM DIAGNOSIS FROM SOCIAL MEDIA
Various techniques and automated engineers are being developed to detect terrorist content on social
media [18], [19]. Malicious data in the form of text, pictures, videos, audio, likes, and re-sharing of posts
spreads terrorist sentiments or infringements or messages for terror clusters, causing massive unrest and
disruptions in the state or country, particularly in certain regions used for spreading propaganda and recruiting
a terrorist army. Figure 2 shows the AI-based terrorist image behavior data prediction. Unethical posts related
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to terrorism and data are collected from online platforms for creating data stores so that features can be
extricated for further intelligent evaluation. The experimental data is collected by scarping the dark web
platform to generate defined fingerprints and criminal activities associated with them. Based on the generated
dataset, the model is trained for the prediction of all events relating to criminal activities, focusing on terrorist-
related actions.
Figure 3 shows the labelling of terrorism-related post and contents. The online platform is surrounded
by illicit activities, but it becomes difficult for normal users to identify and block them. So, terror-related
content is selected for labelling and the results are modelled using intelligent algorithms convolutional neural
network (CNN) and artificial neural network (ANN) [20], [21]. This helps the engines to automatically filter
the malicious posts resembling terror activities and makes the modelled (group) vulnerable [22] as it helps the
person sharing and resharing the post along with comments to highlight the information to the maximum
audience.
Figure 2. AI-based terrorist image behavior data prediction
Figure 3. Labeling of terrorism-related posts and contents
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3. CRIME ANTICIPATION USING ML TECHNIQUES
The comparative study was conducted using Weka, which is open an opensource tool for data mining.
Violent crime trends from the dataset of communities and crime unnormalized and real-time crime statistical
data based on three methods, namely linear regression (LR), additive regression (AR), and decision stump
(DS), were constructed utilizing similar limited sets of characteristics for demonstrating the efficacy of ML
approaches in predicting violent crime patterns of criminal hotspots, the test samples were chosen at random.
LR algorithm shows appreciable results among the listed algorithms and tolerates unpredictability in the test
data to some extent [23]. The crimes of house burglary, street robbery, and battery were examined
retrospectively using an ensemble model to synthesize the findings of logistic regression and neural network
(NN) frameworks using the predictive analytic approach to produce fortnightly and monthly forecasts (based
on previous three years of cybercrime datasets) for the year [1]. ML was used to examine crime predictions.
For the purpose of prediction, crime statistics from the previous 15 years in Vancouver (Canada) were studied.
The accumulation of data, data categorization, pattern recognition, prediction, and visualization are all part of
ML-based criminal investigations. The crime dataset was further analyzed using boosted decision tree (BDT)
and k-nearest neighbor (KNN) methods. In a separate but similar research, [24], [25] looked at 560,000 crime
statistics from 2003 to 2018 and found that using ML algorithms for crime prediction, the studies predicted
crime with an accuracy of 44 per cent to 39 per cent respectively.
The crime dataset from Chicago, the United States. ML and data science (DS) approaches were
applied to predict crime details consisting of parameters (scene positioning, type, date, time, and coordinates).
decision trees (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), and Bayesian
techniques (BT) are used, with the most accurate model training. With an accuracy of about 0.787, the KNN
classification proved to be the most accurate. The authors also utilized several graphics to assist in
comprehending the various features of the Chicago crime dataset to better anticipate, identify, and solve crimes,
resulting in a reduction in the crime rate. Data (taken from Chicago crime statistics, demographic and climatic
data) accumulation, data preprocessing, predictive model development, dataset training, and testing are
included in the proposed system to demonstrate the efficacy of the ML system to forecast violent behaviors,
and crime incidences, and precise attributes of criminals. A deep neural network (DNN) forecasts crime
attributes and occurrences by combining feature-based multi-model data from the environmental context. ML
approaches like regression analysis (RA), kernel density estimation (KDE), and SVM is used in crime
prediction systems [26], [27]. Figure 4 presents the dataflow diagram.
Figure 4. Dataflow diagram
The suggested DNN has an accuracy of 84.25%, whereas the SVM and KDE have an accuracy of
67.01% and 66.33%, indicating that the suggested DNN was much more accurate than the other prediction
models in predicting crime occurrences [5]. The data were analyzed and interpreted using approaches such as
Bayesian neural networks (BNN), and the Levenberg Marquardt algorithm (LMA) [12], and a scaled algorithm,
with the scaled algorithm outperforming the other approaches. Statistical analysis revealed that using the scaled
method, the crime rate could be reduced by 78%, implying an accuracy of 0.78. RapidMiner was used in a
prediction study utilizing ML and historical crime trends in data collection, preparation, analysis, and
visualization in the four primary visualization studies [9]. Big data (BD) offers a high throughput and fault
tolerance, analyzing huge datasets and providing accurate findings, whilst the ML-based naive Bayes (NB)
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method can make superior predictions with the existing datasets. Various data mining (DM) and ML methods
utilizable singminal investigations are presented [6]. This study contributes by emphasizing the techniques
utilized in crime data analytics. The grid-based crime forecasting framework created a series of spatial-
temporal characteristics for a city in Taiwan based on 84 identified geographic locations for anticipating crime
in the next slot (month) for every grid. DNN was determined to be the best model among the numerous ML
techniques, particularly for a feature and attribute learning [28]. Furthermore, the suggested model architecture
exceeded the baseline in terms of crime displacement testing. Figure 5 presents the functionality of the
proposed approach.
Figure 5. Functionality of the proposed approach
4. CRIME PREDICTION APPROACHES (CV, ML, DL)
Alves et al. [29] demonstrated that integrating grey correlation analysis based on a new weighted
k-nearest neighbor (GBWKNN) filling technique with KNN classification improves crime prediction accuracy.
Using the suggested method, the study achieved a 67% accuracy rate. Obuandike et al. [30] classified crime
data into two categories based on complexity, with the KNN method achieving an accuracy of approximately
87%.
Rajesh et al. [18] presented an insight into data mining and ML algorithms using an international
database. With the help of Python and Jupyter Notebook, patterns and predictions were displayed as
visualizations. This analysis aided in the development of suitable counter-terrorism measures, as well as
increased investments, economic growth, and tourism. random forest regressor (RFR) outperformed all other
ML algorithms considered in the study. Using the DT method, [31] obtained an accuracy of 84%. However, in
both situations, a minor change in the data might result in a significant change in the structure. A novel crime
detection approach known as naive Bayes (NB) is used for crime prediction and analysis [32]–[34]. Comes
[11] only had an accuracy rate of 66% in predicting crimes and did not take into account computing speed,
resilience, or scalability which are also important.
The multi-camera model of video surveillance was so well-designed that it can handle all three key
tasks for normal police "stake-out", namely detection, representation, and recognition [35]–[37]. The detecting
section combines video feed from numerous cameras to extract motion trajectories from videos quickly and
accurately. The representation aids in the completion of raw trajectory data in order to create hierarchical,
invariant, and content-rich motion event descriptions. Finally, the recognition section deals with event
classification (such as robbery, as well as possible murder and molestation) and data descriptor identification.
They created a sequence-alignment kernel function to perform sequence data learning to detect suspicious or
possible criminal occurrences for effective recognition. A technique was proposed for distinguishing
individuals for espionage using a novel feature called soft biometry, which incorporates a person's height,
build, facial features, shirt and trousers color, motion behavior, and trajectory record to recognize and monitor
passengers, as well as forecast crime pursuits and deal with some strange human error scenarios where the
perpetrators get away with it [38]. They also conducted examinations with the findings being publicized.
People's behaviors are captured, offering piggyback rides in increasingly remote locations with a given
sequence from event footage. Table 1 summarizes the comparative study of crime prediction techniques with
their accuracy and related findings. In Table 1, we summarized the evaluation models, further demonstrating
qualitative analysis and accuracy.
Crime hotspots, known as severe-crime zones, have a high probability of crime occurrence and present
abnormal events with a high likelihood of detecting criminals. They performed research on predicting crime
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hotspots and implemented their model with google tensor flow. The emphasis is to produce higher value to
demonstrate that the technique is more effective. with similar evaluation parameters, the gated recurrent unit
(GRU) and long short-term memory (LSTM), achieved accuracy (81.5%), precision (86.5%), recall (75%), and
an F1-score (0.8). Both outperform the standard recurrent neural network (RNN) version by a wide margin.
The GRU version showed 2% better performance compared to RNN at receiver operating characteristic (ROC)
area under the curve (AUC) findings. LSTM received the highest AUC score, which was 3% higher than the
GRU version. A spatiotemporal crime network (STCN) is presented [36] which uses a CNN to predict crime
before it happens. From 2010 to 2015, the authors used New York felony datasets (number-311) to test the
STCN. The STCN outperformed the four baselines with an F1-score (88%) and an AUC (92%). Their
suggested model outperformed the other baselines by F1-score and AUC values, and even when the time
window approached 100, it was still better than the others in terms of the effectiveness of working in a densely
populated region.
Table 1. Crime prediction techniques
No Crime prediction
techniques with references
Accuracy Findings
1 RFR [18] 97% High accuracy in previously recorded crimes.
2 DT [15] 83.95% The DT shows good efficiency than NB, along with the same crime
dataset implemented on Weka.
3 KNN (K=10) [39] 87.03% Data has compared to five classification algorithms, finding that the
NB, NN, and KNN algorithms have a better prediction rate than SVM
and DT algorithms.
4 Decision tree (J48) [40] 59.15% Experiments were done on J48 naïve Bayesian and ZeroR by
comparing them.
5 NB [16] 65.59% The comparative study is done based on the accuracy of k-NN, NB,
and DT for the prediction of crimes and criminal behaviors.
6 Naïve Bayes classifier [28] 87.00% NB is used for crime analysis and prediction.
7 SVM [29] 84.37%. Several models have been compared for analyzing the best chance of
predicting hotspots.
8 KNN (K=5) [32] 66.69% By combining GBWKNN and KNN classification approaches better
accuracy is achieved.
9 Proposed word 89.50% Focused on predicting the crime using ML, CV, and DL using crime
statistics for tracing Illicit events channels and criminals’ associations.
5. PROPOSED CONCEPT AND DESIGN FOR CYBERCRIME PREDICTION WITH CRIME
STATISTICS
We assessed the relevance of each approach after discovering and comprehending numerous diverse
ways utilized by security agencies for surveillance reasons. Every surveillance method generates appreciable
results when found actively engaged in communication, like the sting ray used for detecting the geolocation of
a user. So, to track the location based on replicating human approaches continually by self-updating modeling
approach, even though communication is not made, a modern intelligent framework modeling DL, ML, and
CV algorithms for conducting surveillance [41]–[45]. Table 2 contains the key components and processes of
the proposed system. Table 2 contains the key components and processes of the proposed system.
By combining all these capabilities during a preliminary round, we would like to employ closed circuit
television (CCTVs) connected to intelligent automated systems in real-world settings to comprehend the
previously recorded crimes (collected Instances is 8,000), using ML and DL approaches for greater knowledge
of criminality (explaining how, why, and where). We do not just propose building a world-class model to
anticipate crimes; we propose teaching it to comprehend prior crimes in order to better assess and forecast them
based on the utilization of scenario simulations. Following an analysis of the scene and the use of the key
features listed above, the program should conduct at least 90 simulations of the current scenario in front of it,
with the help of previously learned criminal records, to determine and recommend a plan of strategy for alerting
LE personnel. In Figure 6, we provide the terrorist and criminals presence detection models.
- Input tracking: Data is collected from drones, static cameras, voice, and recording devices focused at
suspicious places.
- Mapping with database: Containing profile and features of crime in security agency's databases relating to
dark web (unusual weapon image, suspected criminal image, drug dealers, gangs’ tattoos or marks, financial
fraudulent agent).
- Automated engines: It will search the online presence of these criminals, for mapping with the site, so that
the website and owner activity can be tracked.
- Alert of association: It is generated towards cyber cells or related authorities for collecting evidence.
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- Dynamic database of security agencies connected with OSN: Containing CNN for crawling vulnerable
posts, text, images, and video at OSN to map with Input tracking data [46], [47].
Table 2. Key components and processes of the systems
Components Processes
Root analytics Knowing the number of statistical methodologies able to anticipate future
events.
The instance may range from behavioral intuition to robbing an organization
in future timeframes.
Neural networks Consisting of a huge series of algorithms that assist in the discovery of data
relationships by behaving and associating human cognition.
Replicating biological nerve cells, attempting to think for it.
Anticipating a crime scene.
Automated intelligent engines Engines that must fingerprint antivirus and viruses.
Improving the security of the system by identifying the type of threat and
eliminating it using recognized antivirus.
Continuity of machine’s surveillance in case of broken down.
Prediction of anomaly time series prediction, and decisive approach with
uncertainty.
Data mining in the detection of patterns in criminal’s activity.
Cryptographic algorithms Encrypting the known confidential criminal data in a secure manner.
Utilized to encrypt newly found possible criminal data.
Cyber threat detection and
classification
Classification of threats and criminal conduct like probable terrorist attacks
can be anticipated based on the timeline.
Forensic evidence Organize, analyze, and learn from the data once it has been collected.
NLP Suspicious Speech print identification.
Identification of cyber criminal’s language and comprehension based on
specific features represented using a mathematical formula.
Data collection and analysis Knowing previous crime attributes for casting future crime prediction rates.
Gait analysis To understand posture when walking and research human motion.
To gain a better understanding of a person's usual pace and body mark.
Features To determine an unusual visit to the criminal zone at a specific period,
allowing the system to notify authorities.
The scale of the dark web marketplaces (Silkroad, Alpha Bay, and Pandora) economy was difficult to
determine and was growing all the time. Researchers estimated the Silkroad's sales volume at $360,000 each
day based on scrapes and comments, equating to more than $120 million in a year [48]. The requirements for
meeting the supply of illicit orders generated through dark web platforms are detailed in the Table 3. Our
proposed model helps to track the activities of these associated criminals and agents contacting customers for
delivery, thereby reaching out to the chain of order and criminal events. Table 3 presents the classification,
dealers, agents and percentages of our system, the confusion matrix, and the outlines of graphical statistics of
crime associated with the dark web environment are presented in Figures 7 and 8 respectively. The Table 4 is
performance metrics and outcomes.
Figure 6. Terrorist and criminals’ presence detection model
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Table 3. Dealers and agents meeting chart for illicit business trade and supply
Classification Point of meeting - contact required (dealers and agents) Percentage
Online gambling No 1.7
Weapons trade Yes 2.3
Criminal chat forums May be 2.2
Pornography Yes 3.5
Financial fraud May be 4.9
Anonymity May be 4.7
Ransomware No 3.5
Prostitution Yes 5.3
Human trafficking Yes 5.8
Organ trafficking Yes 5.1
Whistleblower No 4.5
Drug trade Yes 5.2
Financial fraud May be 7.3
Contract killing Yes 1.3
Gangs of Influence Yes 2.3
Live streaming of criminals’ events Yes 3.8
Terrorism propaganda sharing No 5.6
Terrorist recruitment and radicalization Yes 3.4
Sale of antiques Yes 2.8
cyber extortion Yes 3.5
Hacking No 5.2
Cyber-attack activation No 5.3
Industrial applications controlling May be 5.2
others May be 5.6
Figure 7. crime statistics confusion matrix
Figure 8. Crime statistics on dark web platform
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Table 4. Performance metrics and outcomes
S/N Measure Descriptions Outcomes
1 Sensitivity 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 (𝑇𝑃𝑅) = 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 (𝑇𝑃)/(𝑇𝑃 + 𝐹𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 (𝐹𝑁)) 0.7383
2 Specificity 𝑆𝑃𝐶 = 𝑇𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒(𝑇𝑁)/(𝐹𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 (𝐹𝑃) + 𝑇𝑁) 0.9384
3 Precision 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝑣𝑎𝑙𝑢𝑒 (𝑃𝑃𝑉) = 𝑇𝑃/(𝑇𝑃 + 𝐹𝑃) 0.8650
4 Negative predictive value 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝑣𝑎𝑙𝑢𝑒 (𝑁𝑃𝑉) = 𝑇𝑁/(𝑇𝑁 + 𝐹𝑁) 0.9027
5 False positive rate 𝐹𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 (𝐹𝑃𝑅) = 𝐹𝑃/(𝐹𝑃 + 𝑇𝑁) 0.7116
6 False discovery rate 𝐹𝑎𝑙𝑠𝑒 𝑑𝑖𝑠𝑐𝑜𝑣𝑒𝑟𝑦 𝑟𝑎𝑡𝑒 (𝐹𝐷𝑅) = 𝐹𝑃/(𝐹𝑃 + 𝑇𝑃) 0.7959
7 False negative rate 𝐹𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 (𝐹𝑁𝑅) = 𝐹𝑁/(𝐹𝑁 + 𝑇𝑃) 0.6817
8 Accuracy 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 (𝐴𝐶𝐶) = (𝑇𝑃 + 𝑇𝑁)/(𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 (𝑃)
+ 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 (𝑁))
0.8950
9 F1-score 𝐹1 = 2𝑇𝑃/(2𝑇𝑃 + 𝐹𝑃 + 𝐹𝑁) 0.8001
6. RESULT AND DISCUSSION
The comparison of fortnightly projections of monthly analytical predictions with divides into
day-night datasets, the researchers found, greatly improved the results. Due to its secrecy, the dark web has
long been a target for criminals looking to make money illegally abroad. The current work uses ML, CV, and
DL to forecast crime, and crime stats are offered to track criminal networks and compare the comparative
research with the aspects of the suggested strategy that have been put into practice. The research is based on a
fictitious model for locating terrorists and lawbreakers operating on the dark web who are engaged in drug
dealing, human trafficking, staffing of terrorists, distribution of weapons, execution orders delivered online,
and other illegal activities linked to gangs or organizations with active websites. Utilizing automated machine
characteristics, modeling, and recognition. This experiment is about scraping the dark web site generates
specific signatures and the illicit behaviors connected to them, which is how the exploratory data is gathered.
The system is trained to forecast all criminal activity-related occurrences, with an emphasis on terrorist-related
behaviors, using the provided dataset [49]. No such dataset exits contain records of criminals’ events and
channels like (drug supply, human trafficking, terrorist radicalization and recruitment, weapon delivery, online
killing orders, and fraudulent activities associated with gangs or organizations showing online presence). The
proposed focused on the work of hypothetical model and covered multidimensional illicit events channels with
machine learning and computer vison technique [50].
Image processing technique and feature extraction utilizes ImageNet, one of the largest datasets of
annotated pictures, CNN, a deep learning model that has been essential in enhancing computer vision, learns
patterns that typically appear in images and is then equipped to adjust as new data is analyzed. Both a feature
detector and a feature descriptor, spectrum feature transform (SIFT). SIFT splits an image into a vast number
of localized characteristic vectors, all of which is somewhat robust to changes in light and affine or 3D
projection as well as invariant to image translation, scaling, and rotation. Computer vision linking with image
processing: AI and pattern identification methods for crime prediction are used in the domains of CV and image
processing to acquire Illicit event sequences for extracting useful knowledge from photos, videos, and other
visual inputs. One of the numerous methods used in CV is image synthesis, but other methods as well, including
ML, CNN, and so on, are also used. One of the subfields in the science of CV is image processing and belongs
to the subfield of image computing.
7. CONCLUSION AND FUTURE WORK
The authors concluded that comparing fortnightly forecasts of monthly analysis predictions with splits
into day-night datasets improved the results significantly. Due to its anonymity, the dark web has always
attracted the interest of criminals interested in generating illicit revenues across borders. The present work
predicts crime using ML, CV, and DL with crime statistics to track criminal chains and compare the
comparative study with the implemented features of the given approach. The work is based on a hypothetical
model for tracking dark web criminals and terrorists involved in drug supply, human trafficking, terrorist
radicalization and recruitment, weapon delivery, online killing orders, and fraudulent activities associated with
gangs or organizations showing an online presence. The mapping and identification using automated machine
features will help security agencies investigate the root suppliers of prohibited and illegal items. The
anonymous dark web platform changes with hosting, so it takes time to track it. But criminals also use
digital platforms for promotion or marketing tactics to supply or attract other criminals. Based on digital traces
and evidence, security agencies can track the network. Our future research will begin with the creation of a
machine that can predict and recognize patterns based on geo-location coordinates and the dates of similar
crimes. We also hope to create software that can act as a universal security official, with eyes and ears
everywhere.
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BIOGRAPHIES OF AUTHORS
Romil Rawat is a research scholar and attended several research programs and
received research grants from USA, Germany, Italy, and UK. The author has research alignment
towards cyber security, internet of things, dark web crime analysis and investigation techniques,
and working towards tracing of illicit anonymous contents of cyber terrorism and criminal
activities. He also chaired international conferences and hosted several research events
including national and International Research Schools, PhD colloquium, workshops, training
programs. He also published several research patents. He can be contacted at
rawat.romil@gmail.com and rrawatna@alumnos.unex.es.
Olukayode Ayodele Oki received his PhD from the University of Zululand, South
Africa in 2019. He is a lecturer in the Department of Information Technology at Walter Sisulu
University, South Africa. He has authored more than 30 articles. His research interests include
biologically inspired computation, ICT4D, communication networks, internet of things,
machine learning, data analytics and climate-smart agriculture. He has received several grants
both for research and amp; development and to attend conferences. He is a recipient of the South
Africa National Research Foundation (NRF) rated researcher award, an honorary rosalind
member of the London journal press and a member of the IEEE South Africa subsection. He
can be contacted at ooki@wsu.ac.za.
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Sakthidasan Sankaran is a Professor in the Department of Electronics and
Communication Engineering at Hindustan Institute of Technology and Science, India. He
received his B.E. degree from Anna University in 2005, M.Tech. Degree from SRM University
in 2007 and Ph.D. Degree from Anna University in 2016. He is a senior member of IEEE for
the past 10 years and a member of various professional bodies. He is an active reviewer in
Elsevier journals and an editorial board member in various international journals. His research
interests include image processing, wireless networks, cloud computing and antenna design. He
has published more than 70 papers in Referred journals and International Conferences. He has
also published three books to his credit. He can be contacted at sakthidasan.apec@gmail.com.
Hector Florez obtained Ph.D. in Engineering, M.Sc. in Information and
Communication Sciences, M.Sc. in Management, B.Sc. in Electronic Engineering, B.Sc.
in Computing Engineering, and B.Sc. in Mathematics. He is a full professor at the Francisco
Jose de Caldas District University, Bogota Colombia. He can be contacted at email:
haflorezf@udistrital.edu.co.
Sunday Adeola Ajagbe is a Ph.D candidate at the Department of Computer
Engineering, Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria and
a Lecturer, a First Technical University, Ibadan, Nigeria. He obtained MSc and BSc in
Information Technology and Communication Technology respectively at the National Open
University of Nigeria (NOUN), and his Postgraduate Diploma in Electronics and Electrical
Engineering at LAUTECH. His specialization includes Artificial Intelligence (AI), Natural
language processing (NLP), Information Security, Data Science, and the Internet of Things
(IoT). He is also licensed by The Council Regulating Engineering in Nigeria (COREN) as a
professional Electrical Engineer, a student member of the Institute of Electrical and Electronics
Engineers (IEEE), and International Association of Engineers (IAENG). He has many
publications to his credit in reputable academic databases. He can be contacted at email:
sunday.ajagbe@tech-u.edu.ng.