This document compares the performance of Random Forest and Logistic Regression machine learning algorithms for password strength classification. Random Forest uses an ensemble of decision trees to classify passwords, while Logistic Regression uses a logistic function to calculate probability and classify passwords. Both algorithms were trained on a dataset of 669,643 passwords labelled as weak, medium or strong. Random Forest had higher prediction accuracy, while Logistic Regression had better time performance. The author concludes Random Forest may be preferable over Logistic Regression for password strength classification based on its performance.
This document describes several C# projects from IEEE 2014, including summaries of each project. The projects cover topics like localizing jammers in wireless networks, network-coding based cloud storage, privacy-preserving search on encrypted cloud data, compatibility-aware cloud service composition, analyzing social media to understand students' experiences, viral marketing in social networks, opportunistic MAC for underwater sensor networks, WLAN monitoring systems, anonymous vehicle positioning in vehicular networks, and information flow control for cloud security.
Distributed Digital Artifacts on the Semantic WebEditor IJCATR
Distributed digital artifacts incorporate cryptographic hash values to URI called trusty URIs in a distributed environment
building good in quality, verifiable and unchangeable web resources to prevent the rising man in the middle attack. The greatest
challenge of a centralized system is that it gives users no possibility to check whether data have been modified and the communication
is limited to a single server. As a solution for this, is the distributed digital artifact system, where resources are distributed among
different domains to enable inter-domain communication. Due to the emerging developments in web, attacks have increased rapidly,
among which man in the middle attack (MIMA) is a serious issue, where user security is at its threat. This work tries to prevent MIMA
to an extent, by providing self reference and trusty URIs even when presented in a distributed environment. Any manipulation to the
data is efficiently identified and any further access to that data is blocked by informing user that the uniform location has been
changed. System uses self-reference to contain trusty URI for each resource, lineage algorithm for generating seed and SHA-512 hash
generation algorithm to ensure security. It is implemented on the semantic web, which is an extension to the world wide web, using
RDF (Resource Description Framework) to identify the resource. Hence the framework was developed to overcome existing
challenges by making the digital artifacts on the semantic web distributed to enable communication between different domains across
the network securely and thereby preventing MIMA.
Exploring and comparing various machine and deep learning technique algorithm...CSITiaesprime
Domain generation algorithm (DGA) is used as the main source of script in different groups of malwares, which generates the domain names of points and will further be used for command-and-control servers. The security measures usually identify the malware but the domain name algorithms will be updating themselves in order to avoid the less efficient older security detection methods. The reason being the older detection methods does not use either the machine learning or deep learning algorithms to detect the DGAs. Thus, the impact of incorporating the machine learning and deep learning techniques to detect the DGA is well discussed. As a result, they can create a huge number of domains to avoid debar and henceforth, block the hackers and zombie systems with the older methods itself. The main purpose of this research work is to compare and analyse by implementing various machine learning algorithms that suits the respective dataset yielding better results. In this research paper, the obtained dataset is pre-processed and the respective data is processed by different machine learning algorithms such as random forest (RF), support vector machine (SVM), Naive Bayes classifier, H20 AutoML, convolutional neural network (CNN), long short-term memory neural network (LSTM) for the classification. It is observed and understood that the LSTM provides a better classification efficiency of 98% and the H20 AutoML method giving the least efficiency of 75%.
This document summarizes a research paper that proposes using machine learning models to detect phishing websites. It begins with an abstract that introduces the growing problem of phishing attacks and how machine learning can help address it. The document then provides more detail on the proposed methodology, which includes collecting a dataset of legitimate and phishing URLs, extracting features from the URLs, and training/comparing several machine learning models (decision trees, random forests, XGBoost, etc.). It finds that the XGBoost model performs best at classifying URLs as legitimate or phishing. The summary concludes by noting this approach could help create effective phishing detection tools, while acknowledging challenges like accounting for evolving attack types and adversarial examples.
Data Security In Relational Database Management SystemCSCJournals
Proving ownerships rights on outsourced relational database is a crucial issue in today\'s internet based application environments and in many content distribution applications. Here mechanism is proposed for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. Watermarking of relational databases as a constrained optimization problem and discus efficient techniques to solve the optimization problem and to handle the constraints. This watermarking technique is resilient to watermark synchronization errors because it uses a partioning approach that does not require marker tuple. This approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. An implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration and insertion attacks.
The document proposes new distributed deduplication systems with higher reliability for secure cloud storage. Data chunks are distributed across multiple cloud servers using a deterministic secret sharing scheme. This improves reliability over traditional deduplication systems that store only one copy of each file. The systems aim to achieve security requirements of data confidentiality and consistency while limiting overhead.
This document proposes new distributed deduplication systems with higher reliability for secure cloud storage. The key points are:
1. Existing deduplication systems improve storage efficiency but reduce reliability as files are only stored once in the cloud.
2. The proposed systems distribute file chunks across multiple cloud servers using secret sharing to improve reliability.
3. Security mechanisms like confidentiality and tag consistency are achieved through deterministic secret sharing rather than encryption.
This document describes several C# projects from IEEE 2014, including summaries of each project. The projects cover topics like localizing jammers in wireless networks, network-coding based cloud storage, privacy-preserving search on encrypted cloud data, compatibility-aware cloud service composition, analyzing social media to understand students' experiences, viral marketing in social networks, opportunistic MAC for underwater sensor networks, WLAN monitoring systems, anonymous vehicle positioning in vehicular networks, and information flow control for cloud security.
Distributed Digital Artifacts on the Semantic WebEditor IJCATR
Distributed digital artifacts incorporate cryptographic hash values to URI called trusty URIs in a distributed environment
building good in quality, verifiable and unchangeable web resources to prevent the rising man in the middle attack. The greatest
challenge of a centralized system is that it gives users no possibility to check whether data have been modified and the communication
is limited to a single server. As a solution for this, is the distributed digital artifact system, where resources are distributed among
different domains to enable inter-domain communication. Due to the emerging developments in web, attacks have increased rapidly,
among which man in the middle attack (MIMA) is a serious issue, where user security is at its threat. This work tries to prevent MIMA
to an extent, by providing self reference and trusty URIs even when presented in a distributed environment. Any manipulation to the
data is efficiently identified and any further access to that data is blocked by informing user that the uniform location has been
changed. System uses self-reference to contain trusty URI for each resource, lineage algorithm for generating seed and SHA-512 hash
generation algorithm to ensure security. It is implemented on the semantic web, which is an extension to the world wide web, using
RDF (Resource Description Framework) to identify the resource. Hence the framework was developed to overcome existing
challenges by making the digital artifacts on the semantic web distributed to enable communication between different domains across
the network securely and thereby preventing MIMA.
Exploring and comparing various machine and deep learning technique algorithm...CSITiaesprime
Domain generation algorithm (DGA) is used as the main source of script in different groups of malwares, which generates the domain names of points and will further be used for command-and-control servers. The security measures usually identify the malware but the domain name algorithms will be updating themselves in order to avoid the less efficient older security detection methods. The reason being the older detection methods does not use either the machine learning or deep learning algorithms to detect the DGAs. Thus, the impact of incorporating the machine learning and deep learning techniques to detect the DGA is well discussed. As a result, they can create a huge number of domains to avoid debar and henceforth, block the hackers and zombie systems with the older methods itself. The main purpose of this research work is to compare and analyse by implementing various machine learning algorithms that suits the respective dataset yielding better results. In this research paper, the obtained dataset is pre-processed and the respective data is processed by different machine learning algorithms such as random forest (RF), support vector machine (SVM), Naive Bayes classifier, H20 AutoML, convolutional neural network (CNN), long short-term memory neural network (LSTM) for the classification. It is observed and understood that the LSTM provides a better classification efficiency of 98% and the H20 AutoML method giving the least efficiency of 75%.
This document summarizes a research paper that proposes using machine learning models to detect phishing websites. It begins with an abstract that introduces the growing problem of phishing attacks and how machine learning can help address it. The document then provides more detail on the proposed methodology, which includes collecting a dataset of legitimate and phishing URLs, extracting features from the URLs, and training/comparing several machine learning models (decision trees, random forests, XGBoost, etc.). It finds that the XGBoost model performs best at classifying URLs as legitimate or phishing. The summary concludes by noting this approach could help create effective phishing detection tools, while acknowledging challenges like accounting for evolving attack types and adversarial examples.
Data Security In Relational Database Management SystemCSCJournals
Proving ownerships rights on outsourced relational database is a crucial issue in today\'s internet based application environments and in many content distribution applications. Here mechanism is proposed for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. Watermarking of relational databases as a constrained optimization problem and discus efficient techniques to solve the optimization problem and to handle the constraints. This watermarking technique is resilient to watermark synchronization errors because it uses a partioning approach that does not require marker tuple. This approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. An implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration and insertion attacks.
The document proposes new distributed deduplication systems with higher reliability for secure cloud storage. Data chunks are distributed across multiple cloud servers using a deterministic secret sharing scheme. This improves reliability over traditional deduplication systems that store only one copy of each file. The systems aim to achieve security requirements of data confidentiality and consistency while limiting overhead.
This document proposes new distributed deduplication systems with higher reliability for secure cloud storage. The key points are:
1. Existing deduplication systems improve storage efficiency but reduce reliability as files are only stored once in the cloud.
2. The proposed systems distribute file chunks across multiple cloud servers using secret sharing to improve reliability.
3. Security mechanisms like confidentiality and tag consistency are achieved through deterministic secret sharing rather than encryption.
Creation of smart spaces and scaling of devices to achieve miniaturization in pervasive computing environments has put forth a question on the degree of security of such devices. Security being a unique challenge in such environments, solution demands scalability, access control, heterogeneity, trust. Most of the existing cryptographic solutions widely in use rely on the hardness of factorization and number theory
problems. With the increase in cryptanalytic attacks these schemes will soon become insecure. We need an alternate security mechanism which is as hard as the existing number theoretic approaches. In this work, we discuss the aspects of Lattice based cryptography as a new dimension of providing security whose strength lies in the hardness of lattice problems. We discuss about a cryptosystem whose security relies on high lattice dimension.
Dear Sir/Ma’am
I am interested to work as a data specialist in your organization. I believe my experience, skills and work attitude will aid your organization in a great way. Please accept my enclosed resume with this letter.
I worked at Accenture for the last four years. My key responsibilities here were to collect, analyse, store and create data. I made sure that these data were accurate and not damaged. As far as my educational background is concerned, I have a bachelor's degree in EXTC. I am excellent at solving problems and have great analytical skills. I am capable of working well with network administration and can explain the technical problems.
I would appreciate if we could meet up for an interview wherein we can discuss more on this. I can be contacted at +919493377607 or you can email me at imtiaz.khan.sw39@gmail.com
Thank You.
Yours sincerely,
Imtiaz Khan
The document describes a proposed system for resume screening using long short-term memory (LSTM) neural networks. The system would extract data from resume PDFs and clean the data before training an LSTM model to make predictions. It would also retrieve and analyze applicant GitHub profiles using web scraping. The trained LSTM model and weights would be stored for use in a web application. The system is intended to help recruiters identify the best candidates more quickly from a large pool of applicants.
Multikeyword Hunt on Progressive GraphsIRJET Journal
1. The document presents a visualization technique for organizing and exploring large document collections by topic. It uses techniques like removing stop words, stemming, identifying frequent words, generating synonyms, and force-directed layout to generate topics from document collections and visualize them in a graph.
2. Key steps include preprocessing the documents by removing stop words and stemming, identifying frequent words, generating synonyms to group related words into topics, using force-directed layout to position topics based on their relationships, and enabling interactive visualization of the topic graph and retrieval of related documents.
3. The proposed technique aims to help users efficiently browse, organize and find relevant information from large document collections with minimal human intervention through an interactive visualized topic graph.
Detection of Phishing Websites using machine Learning AlgorithmIRJET Journal
This document discusses the detection of phishing websites using machine learning algorithms. It begins with an abstract that defines phishing and explains why attackers use it. The introduction provides more details on phishing techniques and the need for anti-phishing detection methods. The document then reviews related work on phishing detection using machine learning features. It proposes using algorithms like artificial neural networks, k-nearest neighbors, support vector machines, and random forests. Features for these algorithms are discussed like URL-based, HTML/JavaScript-based, and domain-based features. The document concludes that machine learning classifiers can help detect phishing websites but future work is still needed to develop more effective detection systems.
This document summarizes a paper that presents a novel method for passive resource discovery in cluster grid environments. The method monitors network packet frequency from nodes' network interface cards to identify nodes with available CPU cycles (<70% utilization) by detecting latency signatures from frequent context switching. Experiments on a 50-node testbed showed the method can consistently and accurately discover available resources by analyzing existing network traffic, including traffic passed through a switch. The paper also proposes algorithms for distributed two-level resource discovery, replication and utilization to optimize resource allocation and access costs in distributed computing environments.
DDSGA: A Data-Driven Semi-Global Alignment Approach for Detecting Masquerade ...1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
ACTOR CRITIC APPROACH BASED ANOMALY DETECTION FOR EDGE COMPUTING ENVIRONMENTSIJCNCJournal
The pivotal role of data security in mobile edge-computing environments forms the foundation for the
proposed work. Anomalies and outliers in the sensory data due to network attacks will be a prominent
concern in real time. Sensor samples will be considered from a set of sensors at a particular time instant as
far as the confidence level on the decision remains on par with the desired value. A “true” on the
hypothesis test eventually means that the sensor has shown signs of anomaly or abnormality and samples
have to be immediately ceased from being retrieved from the sensor. A deep learning Actor-Criticbased
Reinforcement algorithm proposed will be able to detect anomalies in the form of binary indicators and
hence decide when to withdraw from receiving further samples from specific sensors. The posterior trust
value influences the value of the confidence interval and hence the probability of anomaly detection. The
paper exercises a single-tailed normal function to determine the range of the posterior trust metric. The
decision taken by the prediction model will be able to detect anomalies with a good percentage of anomaly
detection accuracy.
Actor Critic Approach based Anomaly Detection for Edge Computing EnvironmentsIJCNCJournal
The pivotal role of data security in mobile edge-computing environments forms the foundation for the
proposed work. Anomalies and outliers in the sensory data due to network attacks will be a prominent
concern in real time. Sensor samples will be considered from a set of sensors at a particular time instant as
far as the confidence level on the decision remains on par with the desired value. A “true” on the
hypothesis test eventually means that the sensor has shown signs of anomaly or abnormality and samples
have to be immediately ceased from being retrieved from the sensor. A deep learning Actor-Criticbased
Reinforcement algorithm proposed will be able to detect anomalies in the form of binary indicators and
hence decide when to withdraw from receiving further samples from specific sensors. The posterior trust
value influences the value of the confidence interval and hence the probability of anomaly detection. The
paper exercises a single-tailed normal function to determine the range of the posterior trust metric. The
decision taken by the prediction model will be able to detect anomalies with a good percentage of anomaly
detection accuracy
IRJET- An Efficient Ranked Multi-Keyword Search for Multiple Data Owners Over...IRJET Journal
This document summarizes and reviews several existing techniques for efficient multi-keyword search over encrypted cloud data from multiple data owners. It discusses methods that build tree-based indexes, use the TF-IDF model to rank results, and employ techniques like counting Bloom filters and the Paillier cryptosystem to enable searches while preserving data privacy. It also reviews schemes for attribute-based encryption to allow hierarchical access to encrypted files in cloud storage.
Parallel and distributed system projects for java and dot netredpel dot com
This document contains summaries of 8 papers related to distributed systems, cloud computing, and information security. The papers cover topics such as stochastic modeling of data center performance in cloud systems, cost-effective privacy preservation of intermediate data sets in the cloud, capacity of data collection in wireless sensor networks, denial-of-service attack detection using multivariate correlation analysis, dynamic resource allocation using virtual machines in cloud computing, secure outsourcing of large-scale systems of linear equations to the cloud, and deanonymization attacks against anonymized social networks.
IRJET- Efficient Privacy-Preserving using Novel Based Secure Protocol in SVMIRJET Journal
This document presents a novel framework for improving privacy and efficiency in support vector machine (SVM) classification. The framework uses a lightweight multiparty random masking protocol to encrypt user data before it is sent to a server for SVM classification. The classification results are then stored in the cloud. A polynomial aggregation protocol is also used to prevent data leakage while maintaining privacy. The proposed approach is evaluated using two real datasets and is shown to achieve higher accuracy and efficiency compared to conventional methods, while ensuring user data privacy.
Charm a cost efficient multi cloud data hosting scheme with high availabilityPvrtechnologies Nellore
The document proposes a data hosting scheme called CHARM that selects multiple cloud storage providers and redundancy strategies to store data with minimized costs and high availability. CHARM intelligently distributes data using replication or erasure coding based on file size and access patterns. It also triggers transitions of storage modes and data redistribution in response to changing access patterns and cloud pricing. Evaluation shows CHARM saves around 20% of costs compared to other schemes and adapts well to data and price changes.
Efficient Association Rule Mining in Heterogeneous Data BaseIJTET Journal
This document summarizes an algorithm for efficiently mining association rules from heterogeneous databases. The algorithm distributes the database across multiple sites while ensuring privacy. Each site locally mines frequent itemsets using an algorithm like Apriori. The sites then securely combine candidate itemsets and check rule confidence to find globally frequent rules meeting minimum support and confidence thresholds. The algorithm uses commutative encryption and a map-reduce model to parallelize the distributed mining efficiently while limiting data disclosure between sites.
Efficient Similarity Search over Encrypted DataIRJET Journal
This document summarizes a research paper that proposes an efficient method for similarity search over encrypted data stored in the cloud. The method uses Locality Sensitive Hashing (LSH) to index the encrypted data and generate "trapdoors" or encodings of search queries. When a user submits a query, the trapdoor is generated and similarity search is performed by finding the similarity between the query trapdoor and the encrypted data indexes stored in the cloud, without decrypting the actual data. The paper outlines the data uploading and query processing steps, which include preprocessing, encryption, trapdoor generation using LSH-based bucketing of n-grams from the query terms, and using Bloom filters for efficient similarity matching during search.
IRJET- Privacy Preserving Cloud Storage based on a Three Layer Security M...IRJET Journal
This document proposes a three-layer security model for privacy-preserving cloud storage. The model uses encryption techniques like AES and Triple DES to encrypt user data before storing it in the cloud. The encrypted data is then divided into blocks that are distributed across different cloud, fog, and local storage locations. This prevents data leakage even if some blocks are lost or accessed. Computational intelligence paradigms help optimize the distribution of data blocks for efficiency and security. The model aims to provide stronger privacy protection compared to traditional cloud storage security methods.
An Introduction to Random Forest and linear regression algorithmsShouvic Banik0139
This presentation aims to provide a comprehensive understanding of the Random Forest and Linear Regression algorithms, their functioning, and significance. It is designed to equip the audience with the knowledge required to apply these algorithms effectively in practical scenarios, and to further enhance their expertise in the field.
Efficient Privacy Preserving Clustering Based Multi Keyword Search IRJET Journal
This document proposes an efficient privacy-preserving clustering-based multi-keyword search system. It uses hierarchical clustering to generate clusters of encrypted documents in the cloud. The system aims to improve search efficiency while maintaining security. It utilizes EM clustering, SHA-1 hashing for deduplication, and a user revocation method. Experimental results show the framework has advantages such as efficient memory and time utilization, secure search over encrypted data, secure data storage, and deduplication.
Role Based Access Control Model (RBACM) With Efficient Genetic Algorithm (GA)...dbpublications
This document summarizes a research paper that proposes a new cloud data security model using role-based access control, encryption, and genetic algorithms. The model uses Token Based Data Security Algorithm (TBDSA) combined with RSA and AES encryption to securely encode, encrypt, and forward cloud data. A genetic algorithm is used to generate encrypted passwords for cloud users. Role managers are assigned to control user roles and data access. The aim is to integrate encoding, encrypting, and forwarding for secure cloud storage while minimizing processing time.
Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
Creation of smart spaces and scaling of devices to achieve miniaturization in pervasive computing environments has put forth a question on the degree of security of such devices. Security being a unique challenge in such environments, solution demands scalability, access control, heterogeneity, trust. Most of the existing cryptographic solutions widely in use rely on the hardness of factorization and number theory
problems. With the increase in cryptanalytic attacks these schemes will soon become insecure. We need an alternate security mechanism which is as hard as the existing number theoretic approaches. In this work, we discuss the aspects of Lattice based cryptography as a new dimension of providing security whose strength lies in the hardness of lattice problems. We discuss about a cryptosystem whose security relies on high lattice dimension.
Dear Sir/Ma’am
I am interested to work as a data specialist in your organization. I believe my experience, skills and work attitude will aid your organization in a great way. Please accept my enclosed resume with this letter.
I worked at Accenture for the last four years. My key responsibilities here were to collect, analyse, store and create data. I made sure that these data were accurate and not damaged. As far as my educational background is concerned, I have a bachelor's degree in EXTC. I am excellent at solving problems and have great analytical skills. I am capable of working well with network administration and can explain the technical problems.
I would appreciate if we could meet up for an interview wherein we can discuss more on this. I can be contacted at +919493377607 or you can email me at imtiaz.khan.sw39@gmail.com
Thank You.
Yours sincerely,
Imtiaz Khan
The document describes a proposed system for resume screening using long short-term memory (LSTM) neural networks. The system would extract data from resume PDFs and clean the data before training an LSTM model to make predictions. It would also retrieve and analyze applicant GitHub profiles using web scraping. The trained LSTM model and weights would be stored for use in a web application. The system is intended to help recruiters identify the best candidates more quickly from a large pool of applicants.
Multikeyword Hunt on Progressive GraphsIRJET Journal
1. The document presents a visualization technique for organizing and exploring large document collections by topic. It uses techniques like removing stop words, stemming, identifying frequent words, generating synonyms, and force-directed layout to generate topics from document collections and visualize them in a graph.
2. Key steps include preprocessing the documents by removing stop words and stemming, identifying frequent words, generating synonyms to group related words into topics, using force-directed layout to position topics based on their relationships, and enabling interactive visualization of the topic graph and retrieval of related documents.
3. The proposed technique aims to help users efficiently browse, organize and find relevant information from large document collections with minimal human intervention through an interactive visualized topic graph.
Detection of Phishing Websites using machine Learning AlgorithmIRJET Journal
This document discusses the detection of phishing websites using machine learning algorithms. It begins with an abstract that defines phishing and explains why attackers use it. The introduction provides more details on phishing techniques and the need for anti-phishing detection methods. The document then reviews related work on phishing detection using machine learning features. It proposes using algorithms like artificial neural networks, k-nearest neighbors, support vector machines, and random forests. Features for these algorithms are discussed like URL-based, HTML/JavaScript-based, and domain-based features. The document concludes that machine learning classifiers can help detect phishing websites but future work is still needed to develop more effective detection systems.
This document summarizes a paper that presents a novel method for passive resource discovery in cluster grid environments. The method monitors network packet frequency from nodes' network interface cards to identify nodes with available CPU cycles (<70% utilization) by detecting latency signatures from frequent context switching. Experiments on a 50-node testbed showed the method can consistently and accurately discover available resources by analyzing existing network traffic, including traffic passed through a switch. The paper also proposes algorithms for distributed two-level resource discovery, replication and utilization to optimize resource allocation and access costs in distributed computing environments.
DDSGA: A Data-Driven Semi-Global Alignment Approach for Detecting Masquerade ...1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
ACTOR CRITIC APPROACH BASED ANOMALY DETECTION FOR EDGE COMPUTING ENVIRONMENTSIJCNCJournal
The pivotal role of data security in mobile edge-computing environments forms the foundation for the
proposed work. Anomalies and outliers in the sensory data due to network attacks will be a prominent
concern in real time. Sensor samples will be considered from a set of sensors at a particular time instant as
far as the confidence level on the decision remains on par with the desired value. A “true” on the
hypothesis test eventually means that the sensor has shown signs of anomaly or abnormality and samples
have to be immediately ceased from being retrieved from the sensor. A deep learning Actor-Criticbased
Reinforcement algorithm proposed will be able to detect anomalies in the form of binary indicators and
hence decide when to withdraw from receiving further samples from specific sensors. The posterior trust
value influences the value of the confidence interval and hence the probability of anomaly detection. The
paper exercises a single-tailed normal function to determine the range of the posterior trust metric. The
decision taken by the prediction model will be able to detect anomalies with a good percentage of anomaly
detection accuracy.
Actor Critic Approach based Anomaly Detection for Edge Computing EnvironmentsIJCNCJournal
The pivotal role of data security in mobile edge-computing environments forms the foundation for the
proposed work. Anomalies and outliers in the sensory data due to network attacks will be a prominent
concern in real time. Sensor samples will be considered from a set of sensors at a particular time instant as
far as the confidence level on the decision remains on par with the desired value. A “true” on the
hypothesis test eventually means that the sensor has shown signs of anomaly or abnormality and samples
have to be immediately ceased from being retrieved from the sensor. A deep learning Actor-Criticbased
Reinforcement algorithm proposed will be able to detect anomalies in the form of binary indicators and
hence decide when to withdraw from receiving further samples from specific sensors. The posterior trust
value influences the value of the confidence interval and hence the probability of anomaly detection. The
paper exercises a single-tailed normal function to determine the range of the posterior trust metric. The
decision taken by the prediction model will be able to detect anomalies with a good percentage of anomaly
detection accuracy
IRJET- An Efficient Ranked Multi-Keyword Search for Multiple Data Owners Over...IRJET Journal
This document summarizes and reviews several existing techniques for efficient multi-keyword search over encrypted cloud data from multiple data owners. It discusses methods that build tree-based indexes, use the TF-IDF model to rank results, and employ techniques like counting Bloom filters and the Paillier cryptosystem to enable searches while preserving data privacy. It also reviews schemes for attribute-based encryption to allow hierarchical access to encrypted files in cloud storage.
Parallel and distributed system projects for java and dot netredpel dot com
This document contains summaries of 8 papers related to distributed systems, cloud computing, and information security. The papers cover topics such as stochastic modeling of data center performance in cloud systems, cost-effective privacy preservation of intermediate data sets in the cloud, capacity of data collection in wireless sensor networks, denial-of-service attack detection using multivariate correlation analysis, dynamic resource allocation using virtual machines in cloud computing, secure outsourcing of large-scale systems of linear equations to the cloud, and deanonymization attacks against anonymized social networks.
IRJET- Efficient Privacy-Preserving using Novel Based Secure Protocol in SVMIRJET Journal
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Efficient Similarity Search over Encrypted DataIRJET Journal
This document summarizes a research paper that proposes an efficient method for similarity search over encrypted data stored in the cloud. The method uses Locality Sensitive Hashing (LSH) to index the encrypted data and generate "trapdoors" or encodings of search queries. When a user submits a query, the trapdoor is generated and similarity search is performed by finding the similarity between the query trapdoor and the encrypted data indexes stored in the cloud, without decrypting the actual data. The paper outlines the data uploading and query processing steps, which include preprocessing, encryption, trapdoor generation using LSH-based bucketing of n-grams from the query terms, and using Bloom filters for efficient similarity matching during search.
IRJET- Privacy Preserving Cloud Storage based on a Three Layer Security M...IRJET Journal
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An Introduction to Random Forest and linear regression algorithmsShouvic Banik0139
This presentation aims to provide a comprehensive understanding of the Random Forest and Linear Regression algorithms, their functioning, and significance. It is designed to equip the audience with the knowledge required to apply these algorithms effectively in practical scenarios, and to further enhance their expertise in the field.
Efficient Privacy Preserving Clustering Based Multi Keyword Search IRJET Journal
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Role Based Access Control Model (RBACM) With Efficient Genetic Algorithm (GA)...dbpublications
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Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
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Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
This document discusses the pathogenesis and diagnosis of schizophrenia from the perspective of mitochondrial dysfunction and nonlinear dynamics analysis. It summarizes that:
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Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
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Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
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The International Journal of Mechanical Engineering Research and Technology is an international online journal in English published Quarterly offers a fast publication schedule whilst maintaining a proper peer review and the use of recommended electronic formats for an article delivery expedites the process of All submitted research articles are subjected to an immediate rapid screening by the editors consultation with the Editorial Board or others working in the field as assure that they are likely to be the level of interest and importance of appropriate for the journal.
Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
ugc list of approved journals 02 nov.pdfnareshkotra
Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
This document summarizes a study on using computational fluid dynamics (CFD) to model and analyze the heat transfer performance of ceramic heat exchangers with different duct cross-sectional shapes (rectangular, elliptical, cylindrical). CFD was used to calculate parameters like temperature distribution, velocity distribution, heat transfer rate, and effectiveness. The predicted heat transfer rate from CFD analysis was found to be 15% higher than theoretical calculations. Analysis showed that cylindrical ducts had the highest effectiveness at 62%, followed by elliptical at 55% and rectangular at 52%. The document also provides background on the need to improve energy efficiency and reduce emissions, and discusses objectives, assumptions, and methodology of the CFD modeling and analysis.
Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
The International Journal of Mechanical Engineering Research and Technology is an international online journal published by Quarterly offers fast publication schedule with whilst maintaining rigorous peer review. The use of recommended electronic formats for article delivery expedites and the process of All submitted research articles are subjected to immediate rapid screening by the editors consultation with the Editorial Board or others working in the field of appropriate to ensure that they are likely to be the level of interest and importance of appropriate for the journal.
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Neuro Quantology is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between quantum physics and neuroscience. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Neuro Quantology is published monthly.
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2. Int. J. Mech. Eng. Res. & Tech 202
ISSN 2454 – 535X www.ijmert.com
Vol. 15, Issue. 3, sep 2023
Int. J. Mech. Eng. Res. & Tech 20221
A Comparison Study of Random Forest and Logistic
Regression for Password Strength Classification
To what extent is Random Forest (RF) favourable over Logistic Regression (LR)
based on performance in password strength classification?
By: Ananya Jain
Abstract
Context: Passwords have become the universal method of authentication due to their
simplicity and compatibility across a wide range of systems. However, due to their
wide-spreadness, they have become vulnerable to external attacks like password cracking.
Users are infamously poor at maintaining entropy in their passwords due to their tendency of
including dictionary words, names, places, dates, keyboard patterns and so on in passwords,
making them predictable. Password strength classifiers developed using machine learning
algorithms like Random Forest (RF) and Logistic Regression (LR) can efficiently prevent
attacks by coercing users to create strong passwords.
Subjects and Methods: In this study, I trained two machine learning models to detect the
strength of different passwords. The two models use Random Forest and Logistic Regression
respectively to classify passwords strengths as 0,1 or 2 with 0 being weak and 2 being
strongest. I tested the model on 669,643 independent passwords retrieved from Kaggle and
evaluated the models’ classification against password standards.
Results: Random Forest has higher prediction accuracy whereas Logistic Regression has
better time performance
3. Keywords: password strength modelling, Random Forest, Logistic Regression, machine
learning
1. Introduction
Password Strength Classifiers are algorithms used to assess the strength or effectiveness of
passwords. They are designed to analyse characteristics of a password like length, use of
diverse character classes, and complexity to compute a score indicating the password’s level
of security. Higher score means the password is more effective against external attacks.
The usefulness of password classifiers however lies in their ability to give real-time feedback
to users regarding the strength of the password they are creating. Through this, users are
forced to use more secure passwords for their accounts1
.
Password strength classification is used across a variety of websites during the user
registration process for enforcing strong passwords. These are also extended by password
managers like LastPass2
who use it for assessing the safety of users’ saved passwords and for
suggesting strong passwords. Additionally, password classifiers are paving their way into
education with services like Password Monster that are being used to teach users about good
password habits3
.
An approach to creating these algorithms is through machine learning. Random Forest (RF)
and Logistic Regression (LR) are two machine learning models to classify password strength.
However, it is unclear which model is better based on prediction accuracy and prediction
time. Additionally, variation in performance between RF and RS could differ for different
application and development budgets.
1
“Real Time Password Strength Analysis on a Web Application Using Multiple Machine Learning Approaches –
IJERT.” International Journal of Engineering Research & Technology, 24 December 2020,
https://www.ijert.org/real-time-password-strength-analysis-on-a-web-application-using-multiple-machine-learni
ng-approaches. Accessed 2 July 2023.
2
LastPass. "How Secure is Your Password?" LastPass | Something Went Wrong, lastpass.com/howsecure.php.
4. Int. J. Mech. Eng. Res. & Tech 202
ISSN 2454 – 535X www.ijmert.com
Vol. 15, Issue. 3, sep 2023
Int. J. Mech. Eng. Res. & Tech 20221
Accessed 2 July 2023.
3
PasswordMonster, 3 Mar. 2022, www.passwordmonster.com. Accessed 3 July 2023.
Therefore, this paper seeks to investigate whether Random Forest (RF) outperforms
Logistic Regression (LR) for password strength classification, as measured by prediction
accuracy and prediction time. This research could help developers make comprehensive
decisions on whether a Random Forest or Logistic Regression should be used, which
eliminates investing unnecessary time, computer resources and labour attempting to fit data
into an ‘unsuitable’ machine learning algorithm when a better alternative exists, especially
considering machine learning models can require weeks to develop for real world accuracy.4
2. Theoretical Background
2.1 Password Strength
Password Strength is the measure of a password’s resistance against brute-force attacks and
password cracking. The strength lies in the following characteristics of the password:
complexity, length and unpredictability5
. To fulfil these criteria, the following guidelines must
be adhered:
- Password length to be at least 12 characters, preferably more
- Use of both uppercase and lowercase characters
- Avoid dictionary words, keyboard patterns, repetitive characters, and letter sequences
- Avoid using names, addresses, phone numbers, or other personal details
- Avoid common passwords like (password, qwerty, 123456)
- Use a random combination of letters, numbers and symbols6
For this paper, combinations of the above guidelines are grouped together to form the basis of
weak, medium and strong passwords which are numerically represented as 0, 1 and 2
respectively. The nature of each password strength level is described in the table below
Figure 1: Password Strength Classification
5. Password Strength Reason
(i) patty94 Weak (0) + Combination of letters and numbers
− Less than 12 characters
− Use of name
− Only lowercase letters
− No symbols
(ii) alimagik1 Medium (1) + No identifiable dictionary words
+ Combination of letters and numbers
− No uppercase letters
− No symbols
− Less than 12 characters
(iii) TyWM72UNEex8Q8Y! Strong (2) + More than 12 characters
+ Mix of uppercase and lowercase letters
+ Combination of letters and numbers
+ Random arrangement, no identifiable sequences
+ Symbol present
2.2 Drawbacks of Decision Tree
Although decision trees can be used for password strength classification, they may be deemed
unsuitable due to the problem of overfitting, especially when dealing with high-dimensional
data like passwords.
Figure 2: Decision Tree7
A decision tree is generated by recursive splitting of data, based on the most informative
feature, into decision nodes that are used to make decisions and leaf nodes that determine the
result.8
Overfitting occurs during this recursive process when the decision tree catches
random fluctuations or noise in the data instead of learning the underlying patterns. This
overfitting issue can be solved by Random Forest.9
For this reason, Random Forest has been
6. Int. J. Mech. Eng. Res. & Tech 202
ISSN 2454 – 535X www.ijmert.com
Vol. 15, Issue. 3, sep 2023
Int. J. Mech. Eng. Res. & Tech 20221
chosen instead.
2.3 Drawbacks of Linear Regression
Linear regression cannot be used because it lacks suitability for password strength
classification due its inherent nature of predicting only numerical continuous outcomes,
whereas password classification involves categorisation into discrete classes.
"Decision Tree Classification Algorithm." Www.javatpoint.com,
www.javatpoint.com/machine-learning-decision-tree-classification-algorithm. Accessed 10 July 2023.
8
“What is a Decision Tree.” IBM, https://www.ibm.com/topics/decision-trees. Accessed 10 July 2023.
9
“Decision Tree Algorithm in Machine Learning.” Javatpoint,
https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm. Accessed 10 July 2023.
Figure 3: Linear Regression10
In Linear Regression, a linear regression is assumed between the inputted feature and output.
In the case of password classification, a complex relationship exists between password
feature and password strength, therefore, the linear regression model will not accurately
7. capture the relationship11
. For this reason, Logistic Regression is used instead, which is better
suited to such classification problems.
2.4 Random Forest Algorithm
Random Forest is a supervised machine learning algorithm built on the concept of ensemble
learning, where multiple classifiers are combined to solve complex problems12
. Like the name
suggests, a random forest is a collection of multiple decision trees that are trained on various
data subsets through a technique known as bootstrapping. Multiple decision trees enhance
10
"Linear Regression in Machine Learning - Javatpoint." Www.javatpoint.com,
www.javatpoint.com/linear-regression-in-machine-learning. Accessed 10 July 2023.
11
"Linear Regression in Machine Learning - Javatpoint." Www.javatpoint.com,
www.javatpoint.com/linear-regression-in-machine-learning. Accessed 10 July 2023.
12
"Random Forest Algorithm." Www.javatpoint.com,
www.javatpoint.com/machine-learning-random-forest-algorithm. Accessed 10 July 2023.
accuracy as instead of relying on a single decision tree, the algorithm takes the prediction
from each tree. In order to compute a result, a majority voting is done with each tree’s
prediction13
.
Since each tree is generated independently with different data and attributes, Random Forest
allows parallelisation, meaning the CPU can fully be used to create random forests.
Moreover, as majority voting is carried out, the model’s performance is not heavily affected
by minor changes in the dataset, improving stability of the model. The majority vote also has
the added advantage of resistance to overfitting.
2.4a Working of Random Forest for Password Strength Classification
Figure 4: Random Forest for Password
8. Int. J. Mech. Eng. Res. & Tech 202
ISSN 2454 – 535X www.ijmert.com
Vol. 15, Issue. 3, sep 2023
Int. J. Mech. Eng. Res. & Tech 20221
13
R, Sruthi E. "Random Forest | Introduction to Random Forest Algorithm." Analytics Vidhya, 21 June 2022,
www.analyticsvidhya.com/blog/2021/06/understanding-random-forest/. Accessed 10 July 2023.
During Training: Random data points are selected without replacement from the training data
to build individual decision trees. During construction of the trees, at each node the algorithm
partitions the data until a certain stopping criteria is met.
During Testing: For the same password instance from the training set, every tree individually
predicts based on its individual criteria, as seen in the image above, different trees give
different predictions. These predictions are then considered as votes and the class with the
most votes is then declared as the final predicted class.
2.5 Logistic Regression Algorithm
Logistic Regression is a supervised learning classification algorithm capable of predicting a
target variable’s probability based on dependent variables. The main purpose of the model is
9. −𝑥
to find the best fitting model to describe a relationship between an independent variable and a
dependent variable14
. A logistic function is used to model the dependent variable, hence, the
name logistic regression. This logistic function is represented the sigmoid function as below:
𝑆(𝑥) = 1
1 + 𝑒
Logistic Regression calculates its output using this equation to return a probability value
(between 0 and 1) determining its classification15
. Although logistic regression is traditionally
used for binary classification, it can be extended further to classify three or more classes,
known as Multinomial Binary Classification16
, which is used in this case.
14
"Logistic Regression in Machine Learning." Www.javatpoint.com,
www.javatpoint.com/logistic-regression-in-machine-learning. Accessed 10 July 2023.
15
Pant, Ayush. "Introduction to Logistic Regression." Medium, 22 Jan. 2019,
towardsdatascience.com/introduction-to-logistic-regression-66248243c148#:~:text=Logistic%20regression%20t
ransforms%20its%20output,to%20return%20a%20probability%20value. Accessed 10 July 2023.
16
"Just a Moment..." Just a Moment..,
machinelearningmastery.com/multinomial-logistic-regression-with-python/#:~:text=Logistic%20regression%20i
s%20a%20classification,to%20as%20binary%20classification%20problems. Accessed 10 July 2023.
10. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
As Logistic Regression carries out relatively simple calculations in comparison to other
models, it is extremely quick at classifying unknown records. Moreover, as it draws linear
boundaries between the different classes, the risk of overfitting is minimised.
2.5a Working of Logistic Regression for Password Strength Classification
Figure 5: Logistic Regression for Password Strength Classification
In order to classify the passwords, a One-Vs-Rest classification strategy has been used17
.
17
GeeksforGeeks | A Computer Science Portal for Geeks,
www.geeksforgeeks.org/one-vs-rest-strategy-for-multi-class-classification/. Accessed 10 July 2023.
11. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
During Training: Three different binary classifiers are trained for each output class (Weak,
Medium and Strong). All three models are trained on the same test data, however the labels
of positive or negative differ in each case. Through this, a threshold is calculated of ‘Weak’
or ‘Not Weak’, ‘Medium’ or ‘Not Medium’ and ‘Strong’ or ‘Not Strong’, as seen in the
image above.
During Testing: The password is passed through all three models and each model gives out an
output score indicating the probability of the password belonging to that class. The password
is then predicted to belong to the class with the highest probability.
3. Methodology
3.1 Dataset and Pre-processing
The two models were developed with Google Collaboratory18
using Python. To train and test
the models, the Password Strength Classification dataset from Kaggle was used19
. The dataset
contains a total of 669643 passwords. The CSV file contains the password along with a
strength measure equal to 0, 1 or 2, with 0 being weak. There are 496,801 medium (1)
passwords, accounting for about 74% of all the passwords in the dataset.
Figure 6: Distribution of Dataset
Class Labelled As Count Ratio
Weak 0 89,702 13.395%
Medium 1 496,801 74.189%
Strong 2 83,137 12.415%
Total 669643
18
“Welcome To Colaboratory - Colaboratory.” Google Research, https://colab.research.google.com. Accessed 20
July 2023.
19
"Password Strength Classifier Dataset." Kaggle: Your Machine Learning and Data Science Community,
www.kaggle.com/datasets/bhavikbb/password-strength-classifier-dataset. Accessed 20 July 2023.
12. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
As the data set is unbalanced with an approximately 1:6:1 ratio, the the accuracy is not
enough to establish which algorithm performs better due to the problem of overfitting, hence,
the confusion matrix is further used to calculate the F1-score in order to accurately evaluate
the models’ performances. Additionally, each model’s testing time was also noted.
During the pre-processing, the CSV file is loaded into a DataFrame. Then, the number of
missing values (NaNs) is identified and hence all missing values are deleted. The remaining
data is now converted into a numpy array and shuffled to avoid any biases that may exist in
the ordering of the dataset. Using a function for tokenization and the ‘fit_transform’ method,
the list of passwords is converted into a matrix of TF-IDF features.
Figure 7: Data preprocessing code
13. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
3.2 Programming the Models
For this experiment, the training/testing split had to be the same for a fair comparison. Hence,
the train/test split was 80/20% for both models, meaning 535,711 passwords were used for
training and the remaining 133,928 for testing.
While programming the Random Forest model, the number of decision trees used in this
investigation is 10, specified through the ‘n_estiamtors’ parameter. The criterion is set as
‘entropy’, meaning that the algorithm will be using information gain based on entropy of
class labels to evaluate splits. With ‘random_state = 0’ the algorithm sets the random seed to
ensure reproducibility.
Figure 8: Programming the Random Forest model
Similarly, an instance of the ‘LogisticRegression’ class from scikit-learn20
is used to develop
the Logistic Regression model. This instance, named as ‘log_class’, is the logistic regression
classifier used for predictions
Figure 9: Programming the Logistic Regression model
In each model’s program, I measured the performance as follows:
20
scikit-learn: machine learning in Python — scikit-learn 1.3.0 documentation, https://scikit-learn.org/stable/.
Accessed 21 July 2023.
14. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
1) F1 Score: calculated so that a single value can be used for comparison of the
algorithms to take into account a good average of the precision and recall21
. Hence,
the F1 Score indicates a good balance between correctly identifying positive cases and
avoiding false positives. A higher F1 Score means better performance22
. It is
calculated for each password strength classification for both the models
2 × (𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑟𝑒𝑐𝑎𝑙𝑙)
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙
2) Prediction Time (seconds): Measured by the difference between the end and start
time for each model’s program. The values were calculated through the use of
Python’s time library, using the time.time() method23
. The start time was computed
after the training and the end time was computed after the testing. Therefore, the
lower the prediction time in seconds, the better the performance. The prediction time
was calculated as whole for both the models.
3.3 Experimental Procedure
1. Complete TF-IDF vectorisation to transform passwords into a numerical matrix.
2. Execute the code for Random Forest, listing the resultant prediction time and
confusion matrix in the observation tables
3. Clear the Google Compute Engine’s memory to prevent lower time values in later
trials by having the training data in memory
4. Repeat steps 2-3 two more times for trial 2 and trial 3
21
"F1 Score in Machine Learning: Intro & Calculation." V7 - AI Data Platform for Computer Vision, 1 2023,
www.v7labs.com/blog/f1-score-guide. Accessed 20 July 2023.
22
Allwright, Stephen. “What is a good F1 score? Simply explained (2022).” Stephen Allwright, 20 April 2022,
https://stephenallwright.com/good-f1-score/. Accessed 20 July 2023.
23
“time — Time access and conversions — Python 3.11.4 documentation.” Python Docs,
https://docs.python.org/3/library/time.html. Accessed 21 July 2023.
𝐹1 𝑠𝑐𝑜𝑟𝑒 =
15. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
5. Calculate the F-1 score for each password strength using the confusion matrix from
the three trials and list in the observation tables
6. Repeat steps 2-5 for the Logistic Regression programme
Figure 10: Annotated code corresponding to particular steps in my procedure
16. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
4. Experimental Results
4.1 F-1 Score
The table below compares the classification performances of the Random Forest and Logistic
Regression with respect to the password strength. All values observed from the program were
rounded off to 4 decimal places for higher precision. Three trials were conducted because
both the models have randomness in their initialisation and training process; therefore
conducting three trials ensures that the evaluation is representative and not influenced by a
specific randomisation. This averaged result will, thus, be more indicative of expected
real-world performance.
Figure 11: F-1 Score Results
Strength Random Forest Logistic Regression
Trial 1 Trial 2 Trial 3 Avg. Trial 1 Trial 2 Trial 3 Avg.
Weak 0.9492 0.9484 0.9511 0.9495 0.3825 0.3963 0.3893 0.3893
Medium 0.9864 0.9856 0.9867 0.9862 0.8854 0.8844 0.8860 0.8852
Strong 0.9703 0.9685 0.9699 0.9695 0.7530 0.7389 0.7560 0.7493
17. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
4.2 Prediction Time
The table below compares the prediction time of the Random Forest and Logistic Regression.
Each value in the table has been recorded in seconds elapsed during the program execution,
these lower values are more favourable as they indicate quicker predictions by the model. For
the same reasons as stated under ‘Prediction Accuracy’, four decimal places have been used
and three trails have been conducted.
Figure 12: Prediction Time Results
Time Random Forest Logistic Regression
Trial 1 Trial 2 Trial 3 Avg. Trial 1 Trial 2 Trial 3 Avg.
0.4374 0.6834 0.9799 0.7002 0.0443 0.0440 0.0436 0.0439
4.3 Consolidated Performance Results
Figure 13: Consolidated results
Random Forest Logistic Regression
Macro F-1 Score Time/s Macro F-1 Score Time/s
0.9684 0.7002 0.6746 0.0439
18. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
Prediction Time: 0.7002 seconds
4.4 Analysing Random Forest Performance
Figure 14: Random Forest Performance
My experiment indicates that Random Forest can predict password strength with very good
accuracy with a Macro F1 Score of 0.9684. The accuracy seems to increase as password
strength increases, however after medium strength there seems to be diminishing return in
accuracy, this may be due to the imbalance in the dataset. Nevertheless, the overall accuracy
from weak to strong increases from 0.9495 to 0.9695, therefore, the impact of the unbalanced
dataset on the model is minor. This may be the case as Random Forest can effectively handle
imbalanced data by assigning higher weights to the minority class24
during training, thus
improving classification of weak passwords. This property combined with its ensemble
nature are likely the reason for high accuracy.
24
“Surviving in a Random Forest with Imbalanced Datasets | by Kyoun Huh | SFU Professional Computer
Science.” Medium, 13 February 2021,
https://medium.com/sfu-cspmp/surviving-in-a-random-forest-with-imbalanced-datasets-b98b963d52eb.
Accessed 3 August 2023..
19. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
However, the pros of the Random Forest are also likely the reason for its poor time
performance25
of 0.7002 seconds, a 16 times slower performance in comparison to Logistic
Regression. Its ensemble nature constructs 10 decision trees, each which requires time for
optimisation and growth followed by a voting process.
4.5 Analysing Logistic Regression Performance
Figure 15: Logistic Regression Performance
Once again, the results indicate the same pattern of increasing and decreasing accuracy,
however the variations in Logistic Regression are much more significant. With a macro F1
score of 0.6746, the Logistic Regression model has an average accuracy. This may be due to
the fact Logistic Regression handles categorical features by encoding them26
, however, in the
case of passwords, the categorical variables have complex relationships, making it difficult
for the model to encode them. The unbalanced dataset may also be a huge contributor to
diminishing performance as unlike Random Forest, Logistic Regression can be biassed
25
“Random forest Algorithm in Machine learning: An Overview.” Great Learning,
https://www.mygreatlearning.com/blog/random-forest-algorithm/. Accessed 3 August 2023.
26
Roy, Baijayanta. “All about Categorical Variable Encoding | by Baijayanta Roy.” Towards Data Science,
https://towardsdatascience.com/all-about-categorical-variable-encoding-305f3361fd02. Accessed 3 August
2023.
20. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
towards the majority class27
and struggle to predict the minority classes as seen with the
lower F1 score for weak (0.3893) and strong passwords (0.7493). Despite its accuracy
limitations, Logistic Regression took the lead in prediction time with just 0.0439 seconds.
5. Conclusion
In this paper, the performance of Random Forest and Logistic Regression was compared
using the prediction accuracy and prediction time for classification of password strength.
While the inputted dataset for both the models was identical, differences in general trends for
prediction accuracy and prediction time emerged.
Overall, my experiment shows that Random Forest performs with better accuracy whereas
Logistic Regression performs with better prediction time. Therefore, a performance-time
tradeoff exists in both the models for password strength classification. Hence, the selection of
the model depends on the stakeholder’s priority. In case of lower computational resources and
large time constraints, Logistic Regression would be a better choice, whereas where accuracy
is the deciding factor, Random Forest should be used. Hopefully, this paper will prove useful
to developers who are looking to incorporate machine learning models for password strength
classification in their projects.
27
“Issues using logistic regression with class imbalance, with a case study from credit risk modelling.” American
Institute of Mathematical Sciences, https://www.aimsciences.org/article/doi/10.3934/fods.2019016. Accessed 3
August 2023.
21. A Comparison Study of Random Forest and Logistic Regression for Password Strength Classification
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