https://matjournals.net/engineering/index.php/JONSCN/issue/feedJournal of Network Security Computer Networks2025-10-16T07:15:57+00:00Open Journal Systems<p><strong>JONSCN</strong> is a peer reviewed journal in the discipline of Computer Science published by the MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of fundamental research papers on all areas of Network Security. Network Security consists of the provisions and policies adopted by a network administrator to prevent and monitor unauthorized access, misuse, modification, or denial of a computer network and network-accessible resources.</p>https://matjournals.net/engineering/index.php/JONSCN/article/view/2449Network Traffic Intrusion Detection Applications: Key Parameters and Techniques2025-09-17T04:37:28+00:00Twinkal Yadavtwinkleyadav556@gmail.comAkanksha Dubeytwinkleyadav556@gmail.comAshish Tiwaritwinkleyadav556@gmail.com<p><em>Intrusion Detection Systems (IDS) play a critical role in safeguarding digital infrastructures by identifying and mitigating unauthorized access and malicious activities. This paper presents a comprehensive overview of the various IDS techniques, including signature-based, anomaly-Intrusion Detection Systems (IDS) play a vital role in protecting digital infrastructures by detecting and preventing unauthorized access and malicious activities. This paper provides a comprehensive overview of various IDS techniques, including signature-based, anomaly-based, specification-based, and modern machine learning and deep learning methods. Each approach is assessed based on its decision-making processes, input needs, detection abilities, and performance metrics. The study also examines hybrid and heuristic models that aim to improve detection accuracy by combining the strengths of multiple techniques. A detailed comparison is included in tabular form, outlining the advantages, disadvantages, and limitations of each method. While traditional techniques offer high accuracy for known threats, they often fail to detect new and emerging ones. Conversely, learning-based models are effective at identifying unknown intrusions but face challenges like high false positive rates and increased computational requirements. This analysis serves as a valuable reference for researchers and practitioners to choose or develop suitable IDS strategies based on system needs, data features, and the evolving threat landscape. Based on specification and modern machine learning and deep learning approaches, each method is evaluated in terms of its decision-making patterns, input requirements, detection capabilities, and performance metrics. The study further explores hybrid and heuristic-based models that aim to enhance detection accuracy by leveraging the strengths of multiple techniques. A detailed comparative analysis is provided in tabular form, highlighting the advantages, disadvantages, and limitations of each approach. While traditional methods offer high precision for known attacks, they struggle with detecting novel threats. Conversely, learning-based models excel at identifying unknown intrusions but face challenges such as high false positives and computational overhead. This analysis offers a foundational reference for researchers and practitioners to select or design appropriate IDS strategies based on system requirements, data characteristics, and threat landscapes.</em></p>2025-09-17T00:00:00+00:00Copyright (c) 2025 Journal of Network Security Computer Networkshttps://matjournals.net/engineering/index.php/JONSCN/article/view/2571Using Deep Learning for Behavioral Pattern Recognition for Avoiding Cyberbullying: A Comprehensive Review2025-10-16T07:15:57+00:00Manas Kumar Yogimanas.yogi@gmail.comSadanala Ambica Lakshmimanas.yogi@gmail.comAkula Sri Lakshmimanas.yogi@gmail.comP. Devi Sravanthimanas.yogi@gmail.com<p><em>Cyberbullying poses severe psychological and emotional risks worldwide. Traditional monitoring and rule-based detection methods struggle with scalability and contextual adaptability. This review critically examines deep learning-based behavioral pattern recognition models as a scalable and adaptive solution for cyberbullying detection. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Transformer models enable fine-grained feature extraction from multimodal social data, effectively capturing linguistic, temporal, and visual cues of online aggression. The term “psychometrically validated” is replaced with “empirically validated through benchmarked datasets,” ensuring methodological transparency. Comparative analyses of recent deep learning studies demonstrate accuracy improvements of 5–12% over traditional models when applied to benchmark datasets such as Kaggle’s “Cyberbullying Detection” and Twitter Hate Speech datasets. Key challenges, including data imbalance, interpretability, and privacy, are linked to emerging research directions involving fairness-aware, explainable, and privacy-preserving deep architectures.</em></p>2025-10-16T00:00:00+00:00Copyright (c) 2025 Journal of Network Security Computer Networks