Adversarially Robust and Interpretable Magecart Malware Detection
Pedro Pereira, Jos\'e Gouveia, Jo\~ao Vitorino, Eva Maia, Isabel Pra\c{c}a

TL;DR
This paper presents a comprehensive approach combining various machine learning models, adversarial training, and interpretability techniques to detect Magecart malware attacks effectively and robustly in real-world online payment systems.
Contribution
It introduces a comparative study of ML models with adversarial training and interpretability for Magecart malware detection, enhancing robustness and explainability.
Findings
High detection accuracy achieved across models
Models demonstrate robustness against adversarial attacks
Enhanced interpretability supports trust and transparency
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Malware Detection Techniques · Adversarial Robustness in Machine Learning · Spam and Phishing Detection
