Article
Automation & Control Systems
Yongming Han, Yue Wang, Yuan Cao, Zhiqiang Geng, Qunxiong Zhu
Summary: This article proposes a novel binary particle swarm-wrapped feature selection optimization framework (BPSWO), which can improve the intrusion detection accuracy of machine learning methods. The proposed method is examined on the public power system from Oak Ridge National Laboratory, USA and the IEEE 57-bus system. Experimental results show that the BPSWO can achieve the state-of-the-art in the detection accuracy, proving the effectiveness and stability of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Information Systems
Md. Sakir Hossain, Naim Hasan, Md. Abdus Samad, Hossain M. D. Shakhawat, Joydeep Karmoker, Foysol Ahmed, K. F. M. Nafiz Fuad, Kwonhue Choi
Summary: This paper proposes a new Android ransomware detection method based on traffic analysis, which utilizes particle swarm optimization (PSO) for selecting traffic characteristics and decision tree and random forest classifiers for data traffic classification. The method significantly improves the detection accuracy and achieves high performance in detecting ransomware and its types.
Article
Computer Science, Information Systems
Sydney Mambwe Kasongo
Summary: In recent years, advances in technologies such as cloud computing, vehicular networks systems, and the Internet of Things (IoT) have led to a spike in the amount of information transmitted through communication infrastructures. Consequently, attackers have increased their efforts to exploit vulnerabilities in network systems. Therefore, it is crucial to enhance the security of these network systems. This study implements an IDS framework using Machine Learning techniques and evaluates its performance using benchmark datasets.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Giuseppe C. Calafiore, Giulia Fracastoro
Summary: This article introduces two novel sparse versions of the classical nearest-centroid classifier, which perform simultaneous feature selection and classification at a linear computational cost. The proposed classifiers select the most relevant features and have low complexity for testing and classifying new samples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Ayoub Mniai, Mouna Tarik, Khalid Jebari
Summary: Credit card transactions have grown, but they have also led to significant financial losses globally. Machine learning has become important in fraud detection, but existing methods are not able to meet the performance requirements for detecting and predicting unusual fraud patterns. This paper proposes a fraud detection framework that addresses the challenges of unbalanced data, irrelevant features, and tight boundary creation. The framework utilizes undersampling, feature selection, and Support Vector Data Description to build the machine learning model, and improves the optimization capability through a modified Particle Swarm Optimization algorithm. Experimental results on real credit card transaction dataset demonstrate the effectiveness of the framework.
Article
Computer Science, Information Systems
Mohammad Noor Injadat, Abdallah Moubayed, Ali Bou Nassif, Abdallah Shami
Summary: The paper introduces an optimized machine learning-based network intrusion detection system, which improves performance through comparison of different techniques and hyper-parameter optimization. Experimental results show that the model significantly reduces training sample and feature set size, achieving detection accuracies over 99%.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2021)
Review
Automation & Control Systems
M. Di Mauro, G. Galatro, G. Fortino, A. Liotta
Summary: Machine Learning techniques are becoming increasingly important in network intrusion detection for uncovering hidden cyber-threats in abnormal flows. However, dealing with the vast diversity and number of features in data traffic is a challenging problem.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Anjum Nazir, Rizwan Ahmed Khan
Summary: The rapid advancements in communication technologies and services have brought new challenges in cybersecurity. Traditional intrusion detection techniques are not sufficient to protect against modern attacks, leading to the need for innovative solutions like machine learning algorithms. Feature selection methods like 'Tabu Search - Random Forest' have shown promising results in improving classification accuracy and reducing false positives in Network Intrusion Detection Systems.
COMPUTERS & SECURITY
(2021)
Article
Computer Science, Artificial Intelligence
Rishav Pramanik, Sourodip Sarkar, Ram Sarkar
Summary: Pneumonia is a major cause of child mortality in income-deprived regions, and its detection remains a challenge in developing countries. In this paper, a CAD system based on deep learning and a meta-heuristic algorithm is proposed for Pneumonia detection from Chest X-rays, which significantly improves the detection ability compared to other frameworks.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Nilesh Kunhare, Ritu Tiwari, Joydip Dhar
Summary: An intrusion detection system is crucial for detecting threats and unauthorized access. This paper proposes a novel feature selection method using a genetic algorithm and hybrid classification with logistic regression and decision tree. Experimental results show that the gray wolf optimization algorithm achieves the best performance with a reduced feature set. The proposed method is compared with existing methods, demonstrating improved performance.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Theyab R. R. Alsenani, Safial Islam Ayon, Sayeda Mayesha Yousuf, Fahad Bin Kamal Anik, Mohammad Ehsan Shahmi Chowdhury
Summary: In the past ten years, the rapid growth of the Internet has led to a surge in cyber-crimes, with phishing websites being a significant threat to user login credentials and credit card information. Statistics show that attackers commonly use platforms such as PayPal, Microsoft, Facebook, eBay, and Amazon for phishing attempts. This study utilizes the Particle Swarm Optimization (PSO) method to detect phishing websites, aiming to improve detection accuracy through feature selection and model optimization.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shubhra Dwivedi
Summary: The study introduces a new multi-swarm adaptive grasshopper optimization algorithm to protect against sophisticated attacks, showcasing strong performance in experimental results.
COGNITIVE SYSTEMS RESEARCH
(2021)
Article
Engineering, Multidisciplinary
Jian Zhu, Jianhua Liu, Yuxiang Chen, Xingsi Xue, Shuihua Sun
Summary: The paper introduces the Binary Restructuring Particle Swarm Optimization (BRPSO) algorithm as an adaptation of the Restructuring Particle Swarm Optimization (RPSO) algorithm for solving discrete optimization problems. Unlike other binary metaheuristic algorithms, BRPSO does not use transfer functions, instead relying on comparison results and a novel perturbation term for the particle updating process. The algorithm requires fewer parameters and exhibits high exploration capability, as demonstrated by experiments on feature selection problems.
Article
Computer Science, Artificial Intelligence
Warda M. Shaban, Asmaa H. Rabie, Ahmed Saleh, M. A. Abo-Elsoud
Summary: COVID-19, a global infectious disease, requires early detection of infected patients for effective treatment and disease control. This paper introduces a new strategy called DBNB, which uses APSO to select informative features for accurate diagnosis of COVID-19 patients. Experimental results show that DBNB outperforms recent COVID-19 diagnose strategies in accuracy and time efficiency.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Information Systems
Danijela Protic, Miomir Stankovic, Radomir Prodanovic, Ivan Vulic, Goran M. Stojanovic, Mitar Simic, Gordana Ostojic, Stevan Stankovski
Summary: Anomaly-based intrusion detection systems classify computer network behavior by identifying deviations from the statistical model of typical behavior. Feature selection and feature scaling are commonly used techniques to improve classifier performance.
Article
Computer Science, Artificial Intelligence
Ankush Jain, Pramod Kumar Singh, Joydip Dhar
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Mathematics, Applied
Ankur Jain, Joydip Dhar, Vijay Gupta
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2020)
Article
Radiology, Nuclear Medicine & Medical Imaging
R. Jenkin Suji, Sarita Singh Bhadouria, Joydip Dhar, W. Wilfred Godfrey
JOURNAL OF DIGITAL IMAGING
(2020)
Review
Computer Science, Artificial Intelligence
Ankush Jain, Surendra Nagar, Pramod Kumar Singh, Joydip Dhar
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Engineering, Multidisciplinary
Kunwer Singh Mathur, Abhay Srivastava, Joydip Dhar
Summary: This work develops and studies an eco-epidemic predator-prey model with media-induced response function for the interaction of humans with adulterated food. The system has three equilibria, including trivial, disease-free, and endemic, with conditions for local stability explored. The impact of delay parameter and infection delay time on stability, as well as sensitivity analysis of R-0 and R-0(*), are investigated.
JOURNAL OF ENGINEERING MATHEMATICS
(2021)
Article
Computer Science, Hardware & Architecture
Nilesh Kunhare, Ritu Tiwari, Joydip Dhar
Summary: An intrusion detection system is crucial for detecting threats and unauthorized access. This paper proposes a novel feature selection method using a genetic algorithm and hybrid classification with logistic regression and decision tree. Experimental results show that the gray wolf optimization algorithm achieves the best performance with a reduced feature set. The proposed method is compared with existing methods, demonstrating improved performance.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Mathematics
Vandana Kumari, Sudipa Chauhan, Nisha Sharma, Sumit Kaur Bhatia, Joydip Dhar
JORDAN JOURNAL OF MATHEMATICS AND STATISTICS
(2020)
Article
Mathematics, Applied
Vijay Kumar, Joydip Dhar, Harbax S. Bhatti
RICERCHE DI MATEMATICA
(2020)
Article
Engineering, Multidisciplinary
Firdos Karim, Sudipa Chauhan, Sumit Kaur Bhatia, Joydip Dhar
INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES
(2020)
Article
Engineering, Multidisciplinary
Vandana Kumari, Sudipa Chauhan, Joydip Dhar
INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES
(2020)
Article
Business, Finance
Firdos Karim, Sudipa Chauhan, Joydip Dhar
QUANTITATIVE FINANCE AND ECONOMICS
(2020)
Article
Mathematics, Applied
Bhanu Gupta, Amit Sharma, Joydip Dhar, Sanjay K. Srivastava
DIFFERENTIAL EQUATIONS & APPLICATIONS
(2020)
Article
Mathematics, Applied
Vijay Kumar, Joydip Dhar, Harbax S. Bhatti
DIFFERENTIAL EQUATIONS AND DYNAMICAL SYSTEMS
(2020)