Adversarially Robust and Interpretable Magecart Malware Detection
For web security practitioners, this work provides a practical, explainable, and adversarially robust detection method for client-side skimming attacks, though it is an incremental application of existing ML techniques.
This paper develops robust and interpretable machine learning models for detecting Magecart skimming attacks, achieving high detection performance on a real-world dataset while maintaining robustness against adversarial evasion attacks.
Magecart skimming attacks have emerged as a significant threat to client-side security and user trust in online payment systems. This paper addresses the challenge of achieving robust and explainable detection of Magecart attacks through a comparative study of various Machine Learning (ML) models with a real-world dataset. Tree-based, linear, and kernel-based models were applied, further enhanced through hyperparameter tuning and feature selection, to distinguish between benign and malicious scripts. Such models are supported by a Behavior Deterministic Finite Automaton (DFA) which captures structural behavior patterns in scripts, helping to analyze and classify client-side script execution logs. To ensure robustness against adversarial evasion attacks, the ML models were adversarially trained and evaluated using attacks from the Adversarial Robustness Toolbox and the Adaptative Perturbation Pattern Method. In addition, concise explanations of ML model decisions are provided, supporting transparency and user trust. Experimental validation demonstrated high detection performance and interpretable reasoning, demonstrating that traditional ML models can be effective in real-world web security contexts.