An Effective Detection Approach for Phishing URL Using ResMLP - Kakade Kohraj,; Tandale Tushar,; Wakte Sangram,; Shinde Prasanna,; Dr. Suryawanshi P.M., (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
The proliferation of phishing and malware-laden URLs poses an ever-increasing threat to Internet users, enterprises, and critical infrastructure. Traditional URL filtering techniques often rely on static blacklists or shallow machine-learning models, which struggle to generalize to novel attack patterns. In this work, we present a full-stack Malicious URL Detection system combining a Next.js front-end with a Python-based back-end that extracts thirty-two lexical, domain-registration, HTML/JavaScript, and reputation features from user-submitted URLs. Paper details the end-to-end architecture, feature engineering strategies, ResMLP training regimen, and comprehensive evaluation, establishing a template for next-generation URL security platforms.
Keywords: URL; Feature Engineering; Residual MLP (ResMLP); Random Forest; Naïve Bayes; Next.js