International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Machine Learning-Augmented Cyber Forensics for Phishing Threat Detection and Attribution |
|---|---|
| ABSTRACT | Most existing phishing detection systems primarily rely on traditional rule-based filters, blacklists, and conventional machine learning models that analyze email content, URLs, and sender reputations to identify malicious activities. However, these approaches often fall short in effectively addressing the dynamic and increasingly sophisticated nature of phishing attacks, typically resulting in limited accuracy rates and a high number of false positives. They also tend to struggle with real-time detection and adaptability to novel phishing techniques.This project introduces a next-generation phishing detection framework using a hybrid deep learning architecture, RNT-J, which combines ResNeXt for advanced feature extraction and Gated Recurrent Units (GRU) for capturing temporal and sequential dependencies in data. Unlike conventional models, RNT-J employs ,enhances feature representation using ensemble learning techniques that integrate Autoencoders and ResNet (EARN), and uses Isolation Forests for effective anomaly detection. This comprehensive approach enables the framework to accurately and efficiently identify phishing attempts in real time, adapt to evolving attack patterns, and provide support for cybercrime forensics. The synergy of these components addresses critical shortcomings in existing systems by enhancing detection robustness, reducing false positives, and improving operational efficiency, positioning RNT-J as a powerful tool for countering phishing threats in today's complex cybersecurity landscape |
| AUTHOR | KUTUKANUR PRAMOD REDDY, DR. M. NAGARATNA Post-Graduate Student, Cyber Forensics and Information Security, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, India Professor, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407015 |
| pdf/15_Machine Learning-Augmented Cyber Forensics for Phishing Threat Detection and Attribution.pdf | |
| KEYWORDS | |
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