Adversarial Malware Can Be Both Evasive and Deceiving: a Gradient-based Attack Against Prediction and Explainability in Windows PE Malware Detection
In the last years, several gradient-based attacks have been developed to disclose vulnerabilities of deep neural models and strengthen evasion and transferability abilities of adversarial examples created in various domains. In Windows PE malware detection, various gradient-based methods have been recently tested to optimize the editing or injection of adversarial bytes in unused file sections, and create adversarial malicious PE files to evade deep neural models developed for malware detection. On the other hand, gradient information is commonly used in eXplainable AI (XAI) to obtain explanations for opaque decision-making processes of deep neural black-boxes, while Adversarial XAI has recently emerged as an Adversarial Learning field to investigate the security landscape of XAI. In this study, we describe a holistic, gradient-based attack method, named GAME4EXE, formulated to optimize editing of adversarial bytes in the DOS Header and DOS Stub sections of Windows PE malware. The optimization is driven by the dual adversary purpose of generating adversarial malware that evades a target deep neural model, and aligning the explanation of the evading decisions with that of a goodware-like explanation. A preliminary evaluation shows that a gradient-based attack can be effectively formulated in the Windows PE malware domain, to equip the evasion of a deep neural model with the capability to coherently deceive its decision process explainer and harden attack detection.