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69 articles for “Attack Detection”
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Research ArticleOpen accessIn the abstractAutoencoder-Based Intrusion Detection for ARP Poisoning in SDN ControllersKago Seemela, Banyatsang Mphago, Thabo Semong, Phephisa MethulaAddress Resolution Protocol (ARP) poisoning is still a critical control plane attack in Software Defined Networks (SDNs), and spoofing of ARP mappings can trick SDNs controller and accomplish man in the middle, eavesdropping and denial of service attacks. Current SDN intrusion detection systems of ARP attacks rely on supervised learning and labelled attack data which is rather expensive and not … …Journal of Computer Science, Vol. 22, Issue 7 · 21 Jul 2026 · 869 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Misuse and Anomaly Intrusion Detection System Using Hybrid Supervised and Unsupervised Data Mining ApproachesHoma Molavi, Mohammad KhanbabaeiIntrusion Detection Systems (IDSs) play a crucial role in monitoring computer network security. Data mining and machine learning techniques facilitate the identification of intrusion patterns within large volumes of data. Hybrid data mining models have become increasingly popular in IDSs due to their enhanced effectiveness. This study presents a novel hybrid IDS that combines misuse and anomaly detection by integrating … …Journal of Computer Science, Vol. 22, Issue 6 · 6 Jul 2026 · 999 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnsemble Learning-Based Hybrid Explainable Intrusion Detection System With SMOTE for Enhanced Detection and Interpretability in CybersecurityMohammad Subhi Al-Batah, Taqieddin AldrousWith cyberattacks becoming increasingly sophisticated, there is a growing need for Intrusion Detection Systems (IDS) that are both accurate and interpretable. This study introduces a hybrid IDS framework that integrates Random Forest (RF) and Light Gradient Boosting Machine (LGBM) classifiers with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. To enhance transparency and trust, the system also incorporates … …Journal of Computer Science, Vol. 22, Issue 5 · 19 May 2026 · 1,277 accessesView PDFCite
Review ArticleOpen accessIn the abstractA Systematic Review of Adversarial Attacks: ML Techniques, Classification and CountermeasuresBhavesh Kumar Sharma, Sanatan Ratna, Rajiv KumarMachine Learning (ML) and Deep Learning (DL)-based technologies have made significant strides in areas such as computer vision, natural language processing, and autonomous systems. Yet they have been applied in high-stake applications, and have been found to be fragile through adversarial attacks-, maliciously crafted small distortions that fool models into making mistakes. From the pioneering work by Szegedy et al. … …Journal of Computer Science, Vol. 22, Issue 5 · 19 May 2026 · 1,497 accessesView PDFCite
Research ArticleOpen accessIn the abstractSmart and Secure Anomaly Detection in IoT Using Quantum Inspired Kernel Ensemble LearningAbrar M. AlajlanThe exponential growth of the Internet of Things networks has greatly increased the attack surface for cyber threats, necessitating the use of strong and perceptive anomaly detection systems. Traditional intrusion detection systems often struggle with high false alarm rates, limited generalization, and inefficiency in processing high-dimensional heterogeneous IoT data. To address this issue, a novel Hybrid Quantum-Enhanced Kernel Ensemble Learning … …Journal of Computer Science, Vol. 22, Issue 3 · 12 Mar 2026 · 1,762 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Distillation GAN Model for Network Intrusion DetectionChandrakala C B, Sai Sreevall, Raghudathesh G PThis study presents a distillation-based Generative Adversarial Network (GAN) model designed for intrusion detection in network systems. A Teacher-Student architecture is proposed, in which a single teacher generator is utilized to train both the teacher discriminator and the student discriminator. The model was trained to identify anomalies present in network traffic intrusions by tuning the min-max loss function in GANs. … …Journal of Computer Science, Vol. 22, Issue 2 · 5 Mar 2026 · 1,549 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdvanced Hierarchical Deep Learning Approach for Intrusion Detection: Hyperparameter Tuning and Performance Evaluation on the CICIDS DatasetAjeet Singh, Azath M, Shahazad Niwazi Qurashi et al.Intrusion Detection Systems (IDS) are essential in the communication security and integrity of contemporary network architectures. This study introduces a novel advanced hierarchical deep learning model for network intrusion detection using the CICIDS dataset-2019. To improve the detection accuracy, the proposed model architecture builds several deep learning layers embedded with hyperparameters' optimization. Additional preprocessing steps, including over-sampling and min-max scaling, … …Journal of Computer Science, Vol. 21, Issue 11 · 26 Dec 2025 · 2,148 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimized Feature Reduction Techniques for Enhanced Network Threat Detection in Wireless Sensor NetworksBikash Kalita, Satyajit SarmahThe security of Wireless Sensor Networks (WSNs) is currently seriously threatened by numerous threats. Consequently, a number of applications are offered to regulate data and information sharing along with the related security features that need to be maintained throughout data transfer. This study suggests an intelligent feature reduction methodology based on machine learning that uses Modified Principal Component Analysis (MPCA) … …Journal of Computer Science, Vol. 21, Issue 8 · 10 Oct 2025 · 2,290 accessesView PDFCite
Review ArticleOpen accessIn the abstractA Succinct Review on Identification of Network Anomalies and Protection in Cyber-Physical SystemsShalini Kumari, Chander Prabha, Prakash Srivastava et al.Advanced cyberattacks outperform traditional threat detection methods in the rapidly expanding cybersecurity field. Advanced Machine Learning (ML) algorithms can detect network traffic and system problems using AI-based Anomaly Detection (AD) for cybersecurity in real-time. Signature-based systems may overlook new and subtle threats. This paper examines Artificial Intelligence (AI) driven AD systems' design, methodology, and efficacy. The process includes data preprocessing … …Journal of Computer Science, Vol. 21, Issue 6 · 29 May 2025 · 5,234 accessesView PDFCite
Research ArticleOpen accessIn the abstractApriori-Based Analysis of Website PhishingRene Clint Gortifacion, Rhoderick Malangsa, Adelfa Diola, Tamar Mejia JuniorPhishing attacks put users' sensitive information at serious risk and are a rising concern in cybersecurity. In order to minimize the possible damage brought on by these assaults, it is essential to detect and categorize phishing websites correctly. In this study, we provide an Apriori-based analysis method for identifying and categorizing website phishing. The Apriori algorithm, which is frequently used … …Journal of Computer Science, Vol. 21, Issue 5 · 7 May 2025 · 5,040 accessesView PDFCite