Abstract:
Phishing presents an ongoing and dynamic threat to Internet users, targeting personal and confidential information. Existing anti-phishing solutions encounter challenges in keeping up with the ever-changing nature of these attacks, leading to performance degradation over time. This study aims to develop an autonomous anti-phishing solution that effectively counters evolving phishing threats through continuous knowledge updates. To address the challenge of detecting the latest phishing attacks, SmartiPhish, an autonomous anti-phishing solution with continuous learning support, is proposed. Utilizing a quantitative research approach, data is collected from trusted third parties at multiple time points to create a valid dataset. The primary outcome is a reinforcement learning solution that leverages a novel deep learning model alongside Alexa rank and community decisions. The innovative use of Graph Neural Networks in the anti-phishing domain, combined with Long-term Recurrent Convolutional Networks, enables SmartiPhish to estimate a website’s phishing probability using URL and HTML content features. Additionally, the study addresses a crucial research gap by developing a reliable method named PhishRepo for collecting and precisely labelling the latest phishing data. SmartiPhish exhibits positive results, achieving a detection accuracy of 96.40%, an f1-score of 96.42%, and an exceptionally low False Negative Rate (FNR) of 0.029. In real-world web environments, the solution outperforms similar solutions and demonstrates enhanced effectiveness against zero-day phishing attacks. Notably, the integration of continuous learning support facilitates a significant 6% improvement in detection accuracy after six weeks. SmartiPhish’s adaptive approach integrates a systematic knowledge acquisition process, enabling dynamic updates of phishing detection features to counter the ever-evolving landscape of phishing attacks. The findings highlight its potential in strengthening cybersecurity measures and provide practical insights for dealing with phishing threats in today’s digital world. Continuously updating its knowledge base, SmartiPhish stands as a strong defence, promising improved protection for Internet users.
Citation:
Ariyadasa, S.N. (2023). An Active self-learning model for deceptive phishing detection [Doctoral dissertation, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/22771