https://www.matjournals.net/engineering/index.php/JoSCNDS <p><strong>JoSCNDS</strong> is a peer reviewed journal of Computer Science domain published by MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of fundamental research papers on all areas of Security in Computer Networks and Distributed Systems. It is focused on the overall Network Securities such as-Firewall, System Intrusion Detection and Prevention, Access Control and Authorization, Authentication, Computer and Network Forensics, Cryptography, Emergency Management, Virus and Content Filtering, Identification, Authentication, Malware Detection, Encryption, File Type Filtering, URL Filtering, Data Loss Prevention (DLP), Intrusion Prevention Systems (IPS), Remote Access VPN, Hyperscale Network Security, Email Security, Cloud Security, IoT Security, Mobile Security. The main aim of JoSCNDS is to focus on Security Issues in Computer Networks and Distributed Systems, ranging from attacks to all kinds of solutions from prevention to detection approaches.</p> en-US Journal of Security in Computer Networks and Distributed Systems https://www.matjournals.net/engineering/index.php/JoSCNDS/article/view/4070 <p><em>Ransomware has become one of the most dangerous cybersecurity threats, causing significant financial losses and compromising sensitive data across individuals and organizations. Traditional signature-based detection techniques are often ineffective against newly emerging and evolving ransomware variants. Consequently, Machine Learning (ML) has gained considerable attention as an effective approach for identifying ransomware based on behavioral patterns and system activities. This literature survey reviews recent research on ransomware detection using machine learning techniques. It analyzes various detection methods, datasets, feature extraction approaches, and machine learning algorithms such as Decision Trees, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Artificial Neural Networks (ANN), and deep learning models. The survey compares the performance of these techniques using evaluation metrics including accuracy, precision, recall, and F1-score. Furthermore, this survey highlights the strengths and limitations of existing approaches, discusses the challenges involved in detecting zero-day ransomware attacks, handling imbalanced datasets, and achieving real-time detection. It also identifies current research gaps and explores future directions, including explainable artificial intelligence (XAI), federated learning, and lightweight machine learning models for edge and cloud environments.</em></p> Gnanamani H Akshata G. B Ananya K Anju K Anusha Copyright (c) 2026 Journal of Security in Computer Networks and Distributed Systems 2026-09-05 2026-09-05 1 10 https://www.matjournals.net/engineering/index.php/JoSCNDS/article/view/4101 <p><em>Critical infrastructure networks, including power grids, transportation systems, healthcare facilities, water distribution networks, and industrial control systems, depend on highly reliable and resilient communication infrastructures to maintain uninterrupted operations. As these networks become increasingly complex and data-driven, traditional networking architectures often struggle to provide the flexibility, centralized visibility, and rapid fault recovery required for mission-critical services. Software-Defined Networking (SDN) has emerged as a promising solution by separating the control plane from the data plane, enabling centralized network management, programmability, and dynamic resource allocation. However, the centralized nature of SDN introduces a significant challenge: the controller can become a single point of failure, potentially leading to network-wide disruptions when faults or cyberattacks occur. This paper proposes a fault-tolerant SDN architecture aimed at ensuring the reliable operation of critical infrastructure networks under adverse conditions. The proposed framework incorporates controller redundancy, real-time state synchronization, distributed consensus mechanisms, intelligent failover strategies, and edge-assisted control functionalities to enhance network resilience. These mechanisms collectively enable continuous service delivery during controller failures, link outages, switch malfunctions, and distributed denial-of-service (DDoS) attacks targeting the control plane. The architecture is designed to support rapid fault detection, automated recovery, and seamless traffic rerouting while maintaining network performance and operational stability. The effectiveness of the proposed architecture is evaluated through simulation-based analysis using key performance indicators such as network availability, recovery time, packet delivery ratio, latency, and throughput. Results indicate that the integration of fault-tolerance mechanisms significantly improves network reliability, reduces service disruption, and strengthens the overall resilience of SDN-enabled critical infrastructure environments. The proposed approach provides a practical foundation for deploying dependable, secure, and highly available communication networks capable of supporting the stringent operational requirements of modern critical infrastructure systems.</em></p> Mission Franklin Copyright (c) 2026 Journal of Security in Computer Networks and Distributed Systems 2026-09-11 2026-09-11 11 25