Detecting Stealthy Web Bots: A Behavioral Analysis Framework for OpenWPM Automation

Shakil Ahmed, Md Mahedi Hasan Rigan, Md. Shohrab Hossain, Anupam Das

Abstract: Nowadays, Web bots are used extensively for tasks like search engine indexing and security assessments, but they can also facilitate malicious activities such as ad fraud and data theft and many more. However, existing approaches are unable to detect more advanced bots, such as those driven by Selenium or OpenWPM, which can conceal their browser fingerprint and imitate human browsing behaviors. In this paper, we propose a novel technique for identifying advanced bots, specifically those using OpenWPM, through behavioral analysis. Our approach considers four browsing behaviors, including mouse movement, mouse click, keystroke, and scrolling. We employ an ensemble of lightweight classification models trained on behavioral features, which are augmented using unsupervised clustering in a novel way to enhance detection performance. The detection system is designed in a modular fashion, making it resilient to missing behavioral data and independent of platform-specific features or tasks, enabling generalizability across diverse web platforms. The proposed approach achieves an F1-score of 98.8%, presenting a promising solution for detecting OpenWPM bots and other human-mimicking bots with improved precision.