https://matjournals.net/engineering/index.php/JCSPIC/issue/feed 2026-09-09T08:39:00+00:00 Open Journal Systems <p><strong>JCSPIC</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 research and review papers based on all areas of security and privacy including Security in Business, Healthcare and Society, Information Security, Communication Security, and Privacy. Topics related to Biometric--based Security, Cryptography Systems, Critical Infrastructure Security, Application Security, Network Security, Data Loss Prevention, Information Security, Cloud Security, End-User Education, Software Development Security, Security Operations, Physical Security, Embedded Security, Data Analytics for Security and Privacy, Integrated Security Design Schemes, Surveillance, Firewalls, Router and Switch Security, Email Filtering, Vulnerability Scanning, Intrusion Detection and Prevention System (IDS/IPS), Host-based Security Tools, Critical Infrastructures and Key Resources. Research Papers related to Cyber Threat Intelligence and Analytic Solutions, such as Big Data, Artificial Intelligence, and Machine Learning, to Perceive, Reason, Learn, and Act against Cyber Adversary Tactics, Techniques, and Procedures will also be considered.</p> https://matjournals.net/engineering/index.php/JCSPIC/article/view/4091 2026-09-09T08:39:00+00:00 Mubeena Banu chandanachandu7571@gmail.com Chandana K chandanachandu7571@gmail.com Nayanashree D chandanachandu7571@gmail.com

<p><em>The rapid growth of online services has led to a significant increase in phishing attacks, making phishing website detection a critical area of cybersecurity research. Machine learning techniques have emerged as an effective solution by enabling faster and more accurate identification of fraudulent websites. This literature survey reviews four recent studies that apply different machine learning approaches, including Random Forest, Support Vector Machine (SVM), Decision Tree, hybrid models, and AI-driven techniques, for phishing website detection. The reviewed works examine various URL-based, domain-related, and webpage content features to distinguish malicious websites from legitimate ones while addressing issues such as changing phishing strategies, feature selection, and real-time detection. A comparative analysis of these studies highlights their methodologies, performance, advantages, and limitations, helping to identify current research trends and existing challenges. Based on the findings, the survey indicates that integrating effective feature engineering with the Random Forest algorithm can improve the accuracy and reliability of real-time phishing detection systems. The study also provides useful insights for designing practical, secure, and user-friendly solutions that strengthen cybersecurity and promote safer web browsing.</em></p>

2026-09-09T00:00:00+00:00 Copyright (c) 2026 Journal of Cyber Security, Privacy Issues and Challenges