This document discusses adversarial machine learning challenges and countermeasures in cybersecurity. It begins by introducing the topic of adversarial machine learning and its threats to cybersecurity systems that incorporate machine learning models. It then reviews related literature on adversarial attacks against machine learning systems. The document explores different types of adversarial attacks, such as evasion attacks and poisoning attacks, and provides real-world examples. It also discusses the motivations and goals of adversaries launching these attacks. Finally, it delves into common attack algorithms and methods used to generate adversarial examples.
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.
An Empirical Study on the Security Measurements of Websites of Jordanian Publ...CSCJournals
Most of the Jordanian universities’ inquiries systems, i.e. educational, financial, administrative, and research systems are accessible through their campus networks. As such, they are vulnerable to security breaches that may compromise confidential information and expose the universities to losses and other risks. At Jordanian universities, security is critical to the physical network, computer operating systems, and application programs and each area has its own set of security issues and risks. This paper presents a comparative study on the security systems at the Jordanian universities from the viewpoint of prevention and intrusion detection. Robustness testing techniques are used to assess the security and robustness of the universities’ online services. In this paper, the analysis concentrates on the distribution of vulnerability categories and identifies the mistakes that lead to a severe type of vulnerability. The distribution of vulnerabilities can be used to avoid security flaws and mistakes.
PREDICTION OF CYBER ATTACK USING DATA SCIENCE TECHNIQUEIRJET Journal
This document discusses predicting cyber attacks using machine learning techniques. It proposes building a machine learning model for anomaly detection to identify fraudulent, suspicious or unusual network activities that could indicate cyber attacks. The model would be built using data science methods like variable identification on a past dataset to train and test various machine learning algorithms. Performance metrics of different algorithms would be calculated and compared to find the most accurate model for predicting four types of attacks: DOS, R2L, U2R and Probe attacks. A graphical user interface would then display the prediction results of network attack detection.
Modification data attack inside computer systems: A critical reviewCSITiaesprime
This paper is a review of types of modification data attack based on computer systems and it explores the vulnerabilities and mitigations. Altering information is a kind of cyber-attack during which intruders interfere, catch, alter, take, or erase critical data on the personal computers (PCs) and applications through using network exploit or by running malicious executable codes on victim's system. One of the most difficult and trendy areas in information security is to protect the sensitive information and secure devices from any kind of threats. Latest advancements in information technology in the field of information security reveal huge amount of budget funded for and spent on developing and addressing security threats to mitigate them. This helps in a variety of settings such as military, business, science, and entertainment. Considering all concerns, the security issues almost always come at first as the most critical concerns in the modern time. As a matter of fact, there is no ultimate security solution; although recent developments in security analysis are finding daily vulnerabilities, there are many motivations to spend billions of dollars to ensure there are vulnerabilities waiting for any kind of breach or exploit to penetrate into the systems and networks and achieve particular interests. In terms of modifying data and information, from old-fashioned attacks to recent cyber ones, all of the attacks are using the same signature: either controlling data streams to easily breach system protections or using non-control-data attack approaches. Both methods can damage applications which work on decision-making data, user input data, configuration data, or user identity data to a large extent. In this review paper, we have tried to express trends of vulnerabilities in the network protocols’ applications.
Titles with Abstracts_2023-2024_Cyber Security.pdfinfo751436
Implementing a cybersecurity project can offer numerous advantages for organizations in today's digitally connected world. Here are some key benefits:
Protection against Cyber Threats: The primary goal of cybersecurity projects is to safeguard an organization's digital assets from various cyber threats such as malware, ransomware, phishing attacks, and more. This protection is crucial for maintaining the integrity, confidentiality, and availability of sensitive information.
Data Privacy Compliance: Many industries have specific regulations and compliance requirements regarding the protection of sensitive data. Implementing cybersecurity measures helps organizations adhere to these regulations, avoiding legal consequences and potential financial penalties.
Business Continuity: Cybersecurity projects often include strategies for disaster recovery and business continuity planning. In the event of a cyberattack or data breach, having a robust cybersecurity infrastructure in place can minimize downtime and ensure that critical business operations continue without significant disruption.
Risk Management: Cybersecurity projects help organizations identify, assess, and manage potential risks associated with their digital assets. This proactive approach allows businesses to make informed decisions about risk mitigation and prioritize resources effectively.
Customer Trust and Reputation: A strong cybersecurity posture can enhance customer trust and protect the reputation of an organization. Customers are more likely to engage with businesses that prioritize the security of their information, leading to increased brand loyalty.
Intellectual Property Protection: For many organizations, intellectual property (IP) is a valuable asset. Cybersecurity measures help protect intellectual property from theft or unauthorized access, ensuring that companies can maintain a competitive edge in the market.
Employee Awareness and Training: Cybersecurity projects often include employee training programs to raise awareness about cybersecurity threats and best practices. Well-informed employees are a crucial line of defense against social engineering attacks and other cyber threats.
Cost Savings: While implementing cybersecurity measures involves an initial investment, it can result in long-term cost savings. The financial impact of a data breach or cyberattack, including potential legal fees, reputation damage, and loss of business, can far exceed the cost of preventive cybersecurity measures.
Cyber Insurance Benefits: Having a robust cybersecurity infrastructure in place may make an organization more eligible for favorable terms and rates on cyber insurance policies, providing an additional layer of financial protection.
Adaptability to Emerging Threats: Cybersecurity projects are dynamic and adaptive, allowing organizations to stay ahead of evolving cyber threats.
Application Threat Modeling In Risk ManagementMel Drews
How to perform threat modeling of software to protect your business, critical assets and communicate your message to your boss and the Board of Directors
THE MESA SECURITY MODEL 2.0: A DYNAMIC FRAMEWORK FOR MITIGATING STEALTH DATA ...IJNSA Journal
The rising complexity of cyber threats calls for a comprehensive reassessment of current security frameworks in business environments. This research focuses on Stealth Data Exfiltration (SDE), a significant cyber threat characterized by covert infiltration, extended undetectability, and unauthorized dissemination of confidential data. Our findings reveal that conventional defense-in-depth strategies often fall short in combating these sophisticated threats, highlighting the immediate need for a shift in information risk management across businesses. The evolving nature of cyber threats, driven by advancements in techniques, such as social engineering, multi-vector attacks, and the emergence of Generative AI, underscores the need for robust, adaptable, and comprehensive security strategies. As we continue to navigate this complex landscape, it is crucial that we stay ahead of the curve, anticipating potential threats, and continually updating our defenses to protect against them.
We propose a shift from traditional perimeter-based, prevention-focused models, which depend on a static attack surface, to a more dynamic framework that prepares for inevitable breaches. This suggested model, known as ‘MESA 2.0 Security Model’, prioritizes swift detection, immediate response, and ongoing resilience, thereby enhancing an organization’s ability to promptly identify and neutralize threats, significantly reducing the consequences of security breaches. This study suggests that businesses adopt a forward-thinking and adaptable approach to security management, which is crucial for staying ahead of the ever-changing cyber threat landscape. By shifting focus from merely preventing incidents to effectively managing them, organizations can better safeguard their vital digital assets against the increasingly complex tactics used by contemporary cyber adversaries. This study provides valuable insights and a solid strategic framework that aims to steer the development of future security practices and policies to effectively address and mitigate advanced persistent threats.
A review: Artificial intelligence and expert systems for cyber securitybijejournal
Artificial intelligence (AI) and expert systems are essential and vital tools to counter potentially dangerous threats
in cyber security. The protection of data requires skilled cyber security technicians for various types of roles. The
essential role of an expert system is to monitor the threats and assist the technician to strengthen security. The
system uses various datasets like a machine and deep learning as well as reinforced learning in order to make
intelligent decisions. The Internet of Things (IoT) is one of the major concerns for cyber security because it is
potentially the second most likely vulnerable link in the cyber security environment because an attacker can easily
gain access to the system by breaching any IoT device that is connected to the system. Still human is the strongest
and potentially the weakest link in the cyber security environment. This review intends to present AI and expert
systems for cyber security
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.
An Empirical Study on the Security Measurements of Websites of Jordanian Publ...CSCJournals
Most of the Jordanian universities’ inquiries systems, i.e. educational, financial, administrative, and research systems are accessible through their campus networks. As such, they are vulnerable to security breaches that may compromise confidential information and expose the universities to losses and other risks. At Jordanian universities, security is critical to the physical network, computer operating systems, and application programs and each area has its own set of security issues and risks. This paper presents a comparative study on the security systems at the Jordanian universities from the viewpoint of prevention and intrusion detection. Robustness testing techniques are used to assess the security and robustness of the universities’ online services. In this paper, the analysis concentrates on the distribution of vulnerability categories and identifies the mistakes that lead to a severe type of vulnerability. The distribution of vulnerabilities can be used to avoid security flaws and mistakes.
PREDICTION OF CYBER ATTACK USING DATA SCIENCE TECHNIQUEIRJET Journal
This document discusses predicting cyber attacks using machine learning techniques. It proposes building a machine learning model for anomaly detection to identify fraudulent, suspicious or unusual network activities that could indicate cyber attacks. The model would be built using data science methods like variable identification on a past dataset to train and test various machine learning algorithms. Performance metrics of different algorithms would be calculated and compared to find the most accurate model for predicting four types of attacks: DOS, R2L, U2R and Probe attacks. A graphical user interface would then display the prediction results of network attack detection.
Modification data attack inside computer systems: A critical reviewCSITiaesprime
This paper is a review of types of modification data attack based on computer systems and it explores the vulnerabilities and mitigations. Altering information is a kind of cyber-attack during which intruders interfere, catch, alter, take, or erase critical data on the personal computers (PCs) and applications through using network exploit or by running malicious executable codes on victim's system. One of the most difficult and trendy areas in information security is to protect the sensitive information and secure devices from any kind of threats. Latest advancements in information technology in the field of information security reveal huge amount of budget funded for and spent on developing and addressing security threats to mitigate them. This helps in a variety of settings such as military, business, science, and entertainment. Considering all concerns, the security issues almost always come at first as the most critical concerns in the modern time. As a matter of fact, there is no ultimate security solution; although recent developments in security analysis are finding daily vulnerabilities, there are many motivations to spend billions of dollars to ensure there are vulnerabilities waiting for any kind of breach or exploit to penetrate into the systems and networks and achieve particular interests. In terms of modifying data and information, from old-fashioned attacks to recent cyber ones, all of the attacks are using the same signature: either controlling data streams to easily breach system protections or using non-control-data attack approaches. Both methods can damage applications which work on decision-making data, user input data, configuration data, or user identity data to a large extent. In this review paper, we have tried to express trends of vulnerabilities in the network protocols’ applications.
Titles with Abstracts_2023-2024_Cyber Security.pdfinfo751436
Implementing a cybersecurity project can offer numerous advantages for organizations in today's digitally connected world. Here are some key benefits:
Protection against Cyber Threats: The primary goal of cybersecurity projects is to safeguard an organization's digital assets from various cyber threats such as malware, ransomware, phishing attacks, and more. This protection is crucial for maintaining the integrity, confidentiality, and availability of sensitive information.
Data Privacy Compliance: Many industries have specific regulations and compliance requirements regarding the protection of sensitive data. Implementing cybersecurity measures helps organizations adhere to these regulations, avoiding legal consequences and potential financial penalties.
Business Continuity: Cybersecurity projects often include strategies for disaster recovery and business continuity planning. In the event of a cyberattack or data breach, having a robust cybersecurity infrastructure in place can minimize downtime and ensure that critical business operations continue without significant disruption.
Risk Management: Cybersecurity projects help organizations identify, assess, and manage potential risks associated with their digital assets. This proactive approach allows businesses to make informed decisions about risk mitigation and prioritize resources effectively.
Customer Trust and Reputation: A strong cybersecurity posture can enhance customer trust and protect the reputation of an organization. Customers are more likely to engage with businesses that prioritize the security of their information, leading to increased brand loyalty.
Intellectual Property Protection: For many organizations, intellectual property (IP) is a valuable asset. Cybersecurity measures help protect intellectual property from theft or unauthorized access, ensuring that companies can maintain a competitive edge in the market.
Employee Awareness and Training: Cybersecurity projects often include employee training programs to raise awareness about cybersecurity threats and best practices. Well-informed employees are a crucial line of defense against social engineering attacks and other cyber threats.
Cost Savings: While implementing cybersecurity measures involves an initial investment, it can result in long-term cost savings. The financial impact of a data breach or cyberattack, including potential legal fees, reputation damage, and loss of business, can far exceed the cost of preventive cybersecurity measures.
Cyber Insurance Benefits: Having a robust cybersecurity infrastructure in place may make an organization more eligible for favorable terms and rates on cyber insurance policies, providing an additional layer of financial protection.
Adaptability to Emerging Threats: Cybersecurity projects are dynamic and adaptive, allowing organizations to stay ahead of evolving cyber threats.
Application Threat Modeling In Risk ManagementMel Drews
How to perform threat modeling of software to protect your business, critical assets and communicate your message to your boss and the Board of Directors
THE MESA SECURITY MODEL 2.0: A DYNAMIC FRAMEWORK FOR MITIGATING STEALTH DATA ...IJNSA Journal
The rising complexity of cyber threats calls for a comprehensive reassessment of current security frameworks in business environments. This research focuses on Stealth Data Exfiltration (SDE), a significant cyber threat characterized by covert infiltration, extended undetectability, and unauthorized dissemination of confidential data. Our findings reveal that conventional defense-in-depth strategies often fall short in combating these sophisticated threats, highlighting the immediate need for a shift in information risk management across businesses. The evolving nature of cyber threats, driven by advancements in techniques, such as social engineering, multi-vector attacks, and the emergence of Generative AI, underscores the need for robust, adaptable, and comprehensive security strategies. As we continue to navigate this complex landscape, it is crucial that we stay ahead of the curve, anticipating potential threats, and continually updating our defenses to protect against them.
We propose a shift from traditional perimeter-based, prevention-focused models, which depend on a static attack surface, to a more dynamic framework that prepares for inevitable breaches. This suggested model, known as ‘MESA 2.0 Security Model’, prioritizes swift detection, immediate response, and ongoing resilience, thereby enhancing an organization’s ability to promptly identify and neutralize threats, significantly reducing the consequences of security breaches. This study suggests that businesses adopt a forward-thinking and adaptable approach to security management, which is crucial for staying ahead of the ever-changing cyber threat landscape. By shifting focus from merely preventing incidents to effectively managing them, organizations can better safeguard their vital digital assets against the increasingly complex tactics used by contemporary cyber adversaries. This study provides valuable insights and a solid strategic framework that aims to steer the development of future security practices and policies to effectively address and mitigate advanced persistent threats.
A review: Artificial intelligence and expert systems for cyber securitybijejournal
Artificial intelligence (AI) and expert systems are essential and vital tools to counter potentially dangerous threats
in cyber security. The protection of data requires skilled cyber security technicians for various types of roles. The
essential role of an expert system is to monitor the threats and assist the technician to strengthen security. The
system uses various datasets like a machine and deep learning as well as reinforced learning in order to make
intelligent decisions. The Internet of Things (IoT) is one of the major concerns for cyber security because it is
potentially the second most likely vulnerable link in the cyber security environment because an attacker can easily
gain access to the system by breaching any IoT device that is connected to the system. Still human is the strongest
and potentially the weakest link in the cyber security environment. This review intends to present AI and expert
systems for cyber security
ONTOLOGY-BASED MODEL FOR SECURITY ASSESSMENT: PREDICTING CYBERATTACKS THROUGH...IJNSA Journal
The prediction of attacks is essential for the prevention of potential risk. Therefore, risk forecasting contributes a lot to the optimization of the information security budget. This article focuses on the ontology and stages of a cyberattack. It introduces the main representatives of the attacking side and describes their motivation.
Cyber security is said to be the most concentrated topic as it helps end user to stay away or stay secure from cyber attacks. Cyber security models are crucial.
More...
http://goo.gl/IwhtP2
A Survey of Security of Multimodal Biometric SystemsIJERA Editor
A biometric system is essentially a pattern recognition system being used in adversarial environment. Since,
biometric system like any conventional security system is exposed to malicious adversaries, who can manipulate
data to make the system ineffective by compromising its integrity. Current theory and design methods of
biometric systems do not take into account the vulnerability to such adversary attacks. Therefore, evaluation of
classical design methods is an open problem to investigate whether they lead to design secure systems. In order
to make biometric systems secure it is necessary to understand and evaluate the threats and to thus develop
effective countermeasures and robust system designs, both technical and procedural, if necessary. Accordingly,
the extension of theory and design methods of biometric systems is mandatory to safeguard the security and
reliability of biometric systems in adversarial environments.
Enhancing Cybersecurity for Mobile Applications A Comprehensive Analysis, Thr...ijtsrd
The rapid proliferation of mobile applications has revolutionized the way individuals interact with technology, offering unprecedented convenience and connectivity. However, this ubiquity has brought about a corresponding surge in cybersecurity vulnerabilities, posing significant risks to user data and privacy. This research paper presents a comprehensive study aimed at fortifying the security of mobile applications through a holistic approach. By analyzing a diverse range of applications across various industries, we identify and categorize common vulnerabilities that undermine the integrity of these platforms. Our research underscores the critical importance of addressing these vulnerabilities and presents a novel risk assessment framework to quantify potential threats. Leveraging a blend of meticulous code reviews, dynamic analysis, and simulated attack scenarios, we provide developers with actionable insights to enhance security measures effectively. Additionally, we offer a set of best practices and guidelines to guide the implementation of robust security protocols during mobile application development. The culmination of our research is a multifaceted methodology that empowers developers to not only identify and rectify vulnerabilities but also proactively build resilient mobile applications. By bridging the gap between cybersecurity theory and practical implementation, this study contributes to a safer digital landscape for mobile users, fostering trust and security in an increasingly interconnected world. Smitraj Gaonkar | Sanjay Indrale "Enhancing Cybersecurity for Mobile Applications: A Comprehensive Analysis, Threat Mitigation, and Novel Framework Development" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59967.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-security/59967/enhancing-cybersecurity-for-mobile-applications-a-comprehensive-analysis-threat-mitigation-and-novel-framework-development/smitraj-gaonkar
Hyperparameters optimization XGBoost for network intrusion detection using CS...IAESIJAI
With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learning algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CICIDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
An Effective Cybersecurity Awareness Training Model: First Defense of an Orga...IRJET Journal
This document discusses the importance of cybersecurity awareness training for organizations and proposes an effective training model. It analyzes how artificial intelligence (AI) can enhance security awareness programs. Specifically, it examines the Technology Acceptance Model (TAM) and how AI-enabled tools like the viCyber system can help design training based on the National Initiative for Cybersecurity Education (NICE) framework. The study concludes that regular, comprehensive security awareness training is critical to address the human factors that can weaken an organization's cyber defenses. AI tools show promise in developing trainings but require further evaluation of their usability and reliability.
Vulnerability Assessment LITERATURE REVIEW. docNuhuHamza
Vulnerability assessment and penetration testing are processes used to evaluate the security of computer systems and networks. Vulnerability assessment identifies weaknesses that could be exploited by threats, while penetration testing simulates cyber attacks to detect vulnerabilities. There are various tools and methods used for assessments and testing, and both processes play an important role in improving network security. However, assessments and tests also have disadvantages like prevalence of vulnerabilities found and challenges in data collection and analysis. Different models and frameworks provide guidance for conducting assessments and tests systematically.
Trust Metric-Based Anomaly Detection Via Deep Deterministic Policy Gradient R...IJCNCJournal
Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.
Trust Metric-Based Anomaly Detection via Deep Deterministic Policy Gradient R...IJCNCJournal
Addressing real-time network security issues is paramount due to the rapidly expanding IoT jargon. The erratic rise in usage of inadequately secured IoT- based sensory devices like wearables of mobile users, autonomous vehicles, smartphones and appliances by a larger user community is fuelling the need for a trustable, super-performant security framework. An efficient anomaly detection system would aim to address the anomaly detection problem by devising a competent attack detection model. This paper delves into the Deep Deterministic Policy Gradient (DDPG) approach, a promising Reinforcement Learning platform to combat noisy sensor samples which are instigated by alarming network attacks. The authors propose an enhanced DDPG approach based on trust metrics and belief networks, referred to as Deep Deterministic Policy Gradient Belief Network (DDPG-BN). This deep-learning-based approach is projected as an algorithm to provide “Deep-Defense” to the plethora of network attacks. Confidence interval is chosen as the trust metric to decide on the termination of sensor sample collection. Once an enlisted attack is detected, the collection of samples from the particular sensor will automatically cease. The evaluations and results of the experiments highlight a better detection accuracy of 98.37% compared to its counterpart conventional DDPG implementation of 97.46%. The paper also covers the work based on a contemporary Deep Reinforcement Learning (DRL) algorithm, the Actor Critic (AC). The proposed deep learning binary classification model is validated using the NSL-KDD dataset and the performance is compared to a few deep learning implementations as well.
A predictive framework for cyber security analytics using attack graphsIJCNCJournal
Security metrics serve as a powerful tool for organizations to understand the effectiveness of protecting computer networks. However majority of these measurement techniques don’t adequately help corporations to make informed risk management decisions. In this paper we present a stochastic security framework for obtaining quantitative measures of security by taking into account the dynamic attributes associated with vulnerabilities that can change over time. Our model is novel as existing research in attack graph analysis do not consider the temporal aspects associated with the vulnerabilities, such as the availability of exploits and patches which can affect the overall network security based on how the vulnerabilities are interconnected and leveraged to compromise the system. In order to have a more realistic representation of how the security state of the network would vary over time, a nonhomogeneous model is developed which incorporates a time dependent covariate, namely the vulnerability age. The daily transition-probability matrices are estimated using Frei's Vulnerability Lifecycle model. We also leverage the trusted CVSS metric domain to analyze how the total exploitability and impact measures evolve over a time period for a given network.
DETECTION OF ATTACKS IN WIRELESS NETWORKS USING DATA MINING TECHNIQUESIAEME Publication
With the progressive increase of network application and electronic devices (computer, mobile phones, android, etc), attack and intrusion detection is becoming a very challenging task in cybercrime detection area. in this context, most of existing approaches of attack detection rely mainly on a finite set of attacks. However, these solutions are vulnerable, that is, they fail in detecting some attacks when sources of information’s are ambiguous or imperfect. But, few approaches started investigating toward this direction. Following this trends, this paper investigates the role of machine learning approach (ANN, SVM) in detecting TCP connection traffic as normal or suspicious one. But, using ANN and SVM is an expensive technique individually. In this paper, combining two classifiers has been proposed, where artificial neural network (ANN) classifier and support vector machine (SVM) were employed. Additionally, our proposed solution allows visualizing obtained classification results. Accuracy of the proposed solution has been compared with other classifier results. Experiments have been conducted with different network connection selected from NSL-KDD DARPA dataset. Empirical results show that combining ANN and SVM techniques for attack detection is a promising direction
Harnessing the Power of Machine Learning in Cybersecurity.pdfCIOWomenMagazine
Combat Machine Learning in Cybersecurity! Explore applications, benefits, & challenges of ML in cybersecurity for improved detection, response, & resilience.
J018127176.publishing paper of mamatha (1)IOSR Journals
This document discusses classifying patterns under attacks and evaluating pattern security. It proposes a framework for assessing pattern security and modeling adversaries to characterize attack situations. The framework aims to provide a more comprehensive understanding of how classifiers behave under adversarial conditions. This can help lead to better design decisions that improve classifier security against considered attacks. Three applications are discussed - spam filtering, intrusion detection, and biometric verification - where pattern classifiers may be vulnerable if adversarial scenarios are not accounted for during design and evaluation.
A SYSTEMATIC REVIEW ON MACHINE LEARNING INSIDER THREAT DETECTION MODELS, DATA...IJNSA Journal
Computers are crucial instruments providing a competitive edge to organizations that have adopted them. Their pervasive presence has presented a novel challenge to information security, specifically threats emanating from privileged employees. Various solutions have been tried to address the vice, but no exhaustive solution has been found. Due to their elusive nature, proactive strategies have been proposed of which detection using Machine Learning models has been favoured. The choice of algorithm, datasets and metrics are cornerstones of model performance and hence, need to be addressed. Although multiple studies on ML for insider threat detection have been done, none has provided a comprehensive analysis of algorithms, datasets and metrics for development of Insider Threat Detection models. This study conducts a comprehensive systematic literature review using reputable databases to answer the research questions posed. Search strings, inclusion and exclusion criteria were set for eligibility of articles published in the last decade.
Survey of Adversarial Attacks in Deep Learning ModelsIRJET Journal
This document discusses adversarial attacks and defenses in deep learning models from an interpretation perspective. It categorizes interpretation strategies into feature-level interpretation and model-level interpretation. Feature-level interpretation techniques like gradient-based methods and influence functions can help understand adversarial attacks. Model-level interpretation of components and representations can also aid in attacking models. Additionally, feature and model-level interpretation can assist in developing defenses through techniques like model robustification, adversarial detection, and representation interpretation. The document outlines algorithms and methodologies for interpreting adversarial machine learning and considers challenges in interpreting adversarial examples.
Cyber Attack Detection and protection using machine learning algorithmNaruVlogs
Cyber attack detection and protection using machine learning algorithms is a rapidly evolving field within cybersecurity. Machine learning techniques offer the ability to analyze large volumes of data in real-time and identify patterns indicative of malicious activity. Here's a general overview of how machine learning can be applied to cyber attack detection and protection:
Data Collection: The first step is to gather relevant data from various sources such as network traffic logs, system logs, firewall logs, and other security devices. This data will serve as the input for the machine learning models.
Feature Engineering: Feature engineering involves selecting and transforming the raw data into a format suitable for machine learning algorithms. This may include extracting features such as IP addresses, timestamps, packet sizes, protocol types, etc., from the raw data.
Model Training: Once the data is prepared, machine learning models can be trained using supervised, unsupervised, or semi-supervised learning techniques.
Supervised Learning: In supervised learning, the model is trained on labeled data, where each data point is associated with a specific class (e.g., normal or malicious). Common supervised learning algorithms for cyber attack detection include Support Vector Machines (SVM), Random Forests, and Neural Networks.
Unsupervised Learning: Unsupervised learning techniques are used when labeled data is scarce or unavailable. These algorithms can detect patterns and anomalies in the data without prior knowledge of specific attack types. Clustering algorithms like K-means or density-based algorithms like DBSCAN are often used in this context.
DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION AND PREVENTION MODEL FOR IOTBA...IJNSA Journal
Defending against Distributed Denial of Service (DDoS) in the Internet of Things (IoT) computing environment is a challenging task. DDoS attacks are type of collective attack in which attackers work together to compromise internet security and services. The resource-constrained devices used in IoT deployments have made it even easier for an attacker to break, because of the vast number of vulnerable IoT devices with significant compute power. This paper proposed an ensemble machine learning (ML) model using the bagging technique to detect and prevent DDoS attacks in the IoT computing environment. We carried out an Machine Learning experiment and evaluated our proposed model with the most recent DDoS attacks (CICDoS2019) dataset. We use seven validation metrics (classification accuracy, precision rate, recall rate, f1-score, Matthews Correlation Coefficient, false negative rate and false positive rate) to evaluate the performance of the proposed model. The results obtained in our experiment shows an improved performance with an overall maximum classification accuracy of 99.75%, precision rate of 99.99%, recall rate of 99.76%, f1-score of 99.87%, Matthews Correlation Coefficient of 0.000000214, false negative rate of 0.24% and 4.42% false positive rate.
ADDRESSING IMBALANCED CLASSES PROBLEM OF INTRUSION DETECTION SYSTEM USING WEI...IJCNCJournal
The main issues of the Intrusion Detection Systems (IDS) are in the sensitivity of these systems toward the errors, the inconsistent and inequitable ways in which the evaluation processes of these systems were often performed. Most of the previous efforts concerned with improving the overall accuracy of these models via increasing the detection rate and decreasing the false alarm which is an important issue. Machine Learning (ML) algorithms can classify all or most of the records of the minor classes to one of the main classes with negligible impact on performance. The riskiness of the threats caused by the small classes and the shortcoming of the previous efforts were used to address this issue, in addition to the need for improving the performance of the IDSs were the motivations for this work. In this paper, stratified sampling method and different cost-function schemes were consolidated with Extreme Learning Machine (ELM) method with Kernels, Activation Functions to build competitive ID solutions that improved the performance of these systems and reduced the occurrence of the accuracy paradox problem. The main experiments were performed using the UNB ISCX2012 dataset. The experimental results of the UNB ISCX2012 dataset showed that ELM models with polynomial function outperform other models in overall accuracy, recall, and F-score. Also, it competed with traditional model in Normal, DoS and SSH classes.
This document provides an overview of a presentation titled "A Machine Learning Approach to Analyze Cloud Computing Attacks" given at the 5th International Conference on Contemporary Computing and Informatics. The presentation discusses introducing machine learning algorithms to detect various types of cloud computing attacks. It reviews previous work applying supervised, unsupervised, and reinforcement learning techniques for attack detection. The presentation concludes that machine learning provides an effective approach for cloud security but that more research is still needed, particularly for real-time attack detection and mitigation.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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Data Collection: The first step is to gather relevant data from various sources such as network traffic logs, system logs, firewall logs, and other security devices. This data will serve as the input for the machine learning models.
Feature Engineering: Feature engineering involves selecting and transforming the raw data into a format suitable for machine learning algorithms. This may include extracting features such as IP addresses, timestamps, packet sizes, protocol types, etc., from the raw data.
Model Training: Once the data is prepared, machine learning models can be trained using supervised, unsupervised, or semi-supervised learning techniques.
Supervised Learning: In supervised learning, the model is trained on labeled data, where each data point is associated with a specific class (e.g., normal or malicious). Common supervised learning algorithms for cyber attack detection include Support Vector Machines (SVM), Random Forests, and Neural Networks.
Unsupervised Learning: Unsupervised learning techniques are used when labeled data is scarce or unavailable. These algorithms can detect patterns and anomalies in the data without prior knowledge of specific attack types. Clustering algorithms like K-means or density-based algorithms like DBSCAN are often used in this context.
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Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.