Abstract
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.
References
Taha, “Intelligent ensemble learning approach for phishing website detection based on weighted soft voting,” Mathematics, vol. 9, no. 21, p. 2799, 2021.
Cao, W. Han, and Y. Le, “Anti-phishing based on automated individual whitelist,” in Proceedings of the 4th ACM workshop on Digital identity management, 2008, pp. 51–60.
Maneesha, K. Rajasekhar, K. Prema Latha, and N. Venkata Prasad, “Detection of phreaking website using various algorithms.” International Research Journal on Advanced Science Hub Vol. 05, Issue 05S May pp305 -313
M. Sharifi and S. H. Siadati, “A phishing sites blacklist generator,” in 2008 IEEE/ACS international conference on computer systems and applications. IEEE, 2008, pp. 840–843.
R Kiruthiga and D Akila. Phishing websites detection using machine learning. International Journal of Recent Technology and Engineering, 8(2):111–114, 2019.
S Carolin Jeeva and Elijah Blessing Rajsingh. Intelligent phishing url detection using association rule mining. Human-centric Computing and Information Sciences, 6(1):1–19, 2016.
Ankit Kumar Jain, Brij B Gupta, et al. Phishing detection: analysis of visual similarity based approaches. Security and Communication Networks, 2017, pp 1-20
SA Al-Saaidah. Detecting phishing emails using machine learning techniques. International Journal of Applied Information Systems. Volume 12 – No. 7, October 2017. Pp 21-24
Ashit Kumar Dutta. Detecting phishing websites using machine learning technique. PloS one, 16(10): e0258361, 2021. Pp 1-17
Moitrayee Chatterjee and Akbar-Siami Namin. Detecting phishing websites through deep reinforcement learning. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), volume 2, pages 227–232. IEEE, 2019.
Ram B Basnet, Andrew H Sung, and Quingzhong Liu. Learning to detect phishing urls. International Journal of Research in Engineering and Technology, 3(6):11–24, 2014.
Lawrence Abrams. What are google chrome extensions?, 2017.
Subasi, Abdulhamit, Esraa Molah, Fatin Almkallawi, and Touseef J. Chaudhery. "Intelligent phishing website detection using random forest classifier." In 2017 International conference on electrical and computing technologies and applications (ICECTA), pp. 1-5. IEEE, 2017.
“UCI Machine Learning Repository: Phishing Websites Data Set,”[Online]. Available: https://archive.ics.uci.edu/ml/datasets/ phishing websites.
Li, Jhen-Hao, and Sheng-De Wang. "PhishBox: An approach for phishing validation and detection." In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 557-564. IEEE, 2017.
Ahmed, Abdulghani Ali, and Nurul Amirah Abdullah. "Real time detection of phishing websites." In 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 1-6. IEEE, 2016.