An Effective Detection Approach for Phishing URL Using ResMLP

• Published in: IEEE access Vol. 12; pp. 79367 - 79382
• Main Authors: Remya, S., Pillai, Manu J., Nair, Kajal K., Rama Subbareddy, Somula, Cho, Yong Yun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Blocklists; classification; Cybercrime; Cybersecurity; Feature extraction; Internet; Multilayer perceptrons; Multilayers; Neural networks; Phishing; Pipeline processing; residual pipelining; Uniform resource locators; URL detection; URLs
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3409049

Phishing websites, mimicking legitimate counterparts, pose significant threats by stealing user information through deceptive Uniform Resource Locators (URLs). Traditional blacklists struggle to identify dynamic URLs, necessitating advanced detection mechanisms. In this study, we propose an effective approach utilizing residual pipelining for phishing URL detection. Our method extracts common URL features and sentiments, employing a residual pipeline comprising convolutional and inverted residual blocks. These resultant features are then fed into a Multi-Layer Perceptron (MLP) for classification. We evaluate the efficacy of our approach against traditional algorithms using a Kaggle dataset. Our results demonstrate superior accuracy, precision, F1 Score, and recall, showcasing its effectiveness in mitigating phishing threats. Utilizing a residual pipeline made up of convolutional and inverted residual blocks, we start our method by identifying similar URL features and sentiments. We also use domain age research to figure out how long URLs have been around. Additionally, the lexical study of URL structure makes our method more useful, resulting in impressive accuracy. With an accuracy of 98.29%, this research highlights the importance of innovative techniques in combating evolving cyber threats. Future research directions could focus on enhancing the model's robustness against adversarial attacks and integrating real-time monitoring for proactive defense strategies.