Leveraging Machine Learning for the Identification of Obfuscated JavaScript in Phishing Attacks

Abstract

JavaScript obfuscation has emerged as a pervasive tactic employed by cybercriminals to conceal malicious code and facilitate phishing attacks. As a language supported by over 95% of modern websites, JavaScript provides a fertile ground for exploitation due to its ubiquity and integration into nearly all web applications. Cyber attackers frequently rely on obfuscation techniques to disguise malicious scripts, thereby evading detection by traditional antivirus software and rendering manual code analysis exceedingly difficult. The complexity of modern obfuscation techniques demands advanced detection methodologies beyond signature-based tools. This research focuses on exploring the interplay between JavaScript obfuscation and phishing, identifying prevalent obfuscation methods, and deploying machine learning (ML) approaches to detect these threats. By leveraging supervised learning algorithms and semantic feature extraction, we demonstrate how ML can be utilized to distinguish between benign and malicious, obfuscated scripts. The study also conducts a comprehensive review of existing tools, methodologies, and academic research addressing this challenge. We propose a robust framework integrating abstract syntax tree (AST) analysis, lexical pattern recognition, and ensemble ML models for enhanced detection accuracy. Additionally, this study outlines key implementation strategies, challenges, and evaluation metrics while providing a critical outlook on future research pathways. The proposed approach promises significant advancements in cybersecurity by improving the precision of threat detection systems, thus reducing the risks posed by obfuscated phishing scripts in web applications.