PAC-X: Fuzzy Explainable AI for Multiclass Malware Detection

Researchers often approach malware detection as a binary classification problem. However, evidence indicates that malware can belong to multiple families simultaneously, and malicious files frequently exhibit numerous benign features. Attackers exploit this by embedding malicious intent within benign features, making malware detection a problem better suited for fuzzy systems. Furthermore, providing explainability for such fuzzy classification remains a significant challenge, requiring specialized Explainable AI (XAI) frameworks. Existing XAI approaches offer insights into model decisions but are vulnerable to adversarial attacks that manipulate features to mislead models. To address these issues, we propose PAC-X, a novel XAI framework for malware detection. PAC-X integrates the Conditional Attention Neural Network (CAN-Net) to deliver comprehensive multifuzzy-class explainability and employs Contextual Fuzzy Clustering (CFC) to extract contextual insights from training data. This framework is resilient to adversarial manipulations, maintaining reliable and interpretable explanations even under adversarial conditions designed to mislead the model. Through extensive evaluations on diverse malware datasets, PAC-X demonstrates superior explainability and robustness compared to state-of-the-art XAI methods. It provides a critical advancement in cybersecurity by addressing the complexities of evasive malware detection and enabling a deeper interpretation of multiclass malware characteristics.