PHISHSNAP-A Chrome Extension Tool used for Detection of Phishing applying Machine Learning - S, Arya Nadh T; P, Binitha; Suresh, Nimmi; S, Pranaya V; Kumar, Unnikrishnan S (Journal of Artificial Intelligence and Capsule Networks, 2024)
This work introduces a novel approach aimed at strengthening the effectiveness of phishing detection systems in the face of evolving cyber threats. Leveraging the power of machine learning-based anomaly detection techniques, this proposed mechanism seeks to significantly enhance both the accuracy and adaptability of current detection methods to effectively combat emerging phishing attacks. Central to this methodology is the utilization of ensemble model mechanisms, which intelligently integrate predictions from a diverse array of machine learning models. Through cautious analysis of URLs utilizing distinct datasets, this system systematically compares and contrasts results with established approaches, thereby enriching the overall detection process. This approach showcases notable improvements in performance metrics, boasting higher success rates that substantially exceed conventional heuristic analysis and blacklist-based detection methodologies. By transcending the limitations inherent in traditional detection strategies, this innovative framework represents a promising leap forward in the ongoing battle against phishing exploits, offering enhanced resilience in safeguarding sensitive user information from malicious cyber threats.
Keywords: Phishing Detection; Machine Learning; Anomaly Detection; Ensemble Models; Cyber Threats; URLs; Datasets; Performance Metrics; Heuristic Analysis; Blacklist-Based Detection; Emerging Threats; Cyber Exploits; User Information; Malicious Threats