ML-Enhanced Web Security System for Detecting and Blocking Spoofing Attacks

Abstract

Web spoofing remains one of the most pervasive cyberattacks, enabling adversaries to mimic legitimate websites to extract sensitive user information such as login credentials, financial details, and personal identity data [1]. Traditional security solutions—including signature-based detection and server-side filtering—often fail to respond effectively to newly emerging and dynamically generated spoofed URLs [4], [11]. To address this gap, the proposed ML-Enhanced Web Security System introduces a client-side defense mechanism powered by machine learning to detect and block spoofing attacks in real time [2]. The system extracts and analyzes multiple discriminative features, including URL lexical patterns, DNS registration details, webpage visual similarity attributes, SSL certificate anomalies, and DOM structural irregularities [3], [8], [17], [9]. A hybrid ML model combines lightweight ensemble classifiers to ensure high detection accuracy while maintaining low computational overhead for seamless deployment in web browsers and endpoint devices [15]. Comprehensive experiments on benchmark phishing and spoofed website datasets demonstrate that the proposed system significantly outperforms existing security filters in terms of precision, false-positive rate, and response speed [6], [19]. Furthermore, the client-side implementation eliminates reliance on external threat databases, enabling proactive protection even against zero-day spoofing websites [12]. This ML-enhanced web security solution provides a scalable and user-oriented shield against spoofing attacks, supporting safer digital experiences for users across financial, e-commerce, and enterprise environments.