Advxai in Malware Analysis Framework: Balancing Explainability with Security

With the increased use of Artificial Intelligence (AI) in malware analysis there is also an increased need to understand the decisions models make when identifying malicious artifacts. Explainable AI (XAI) becomes the answer to interpreting the decision-making process that AI malware analysis models use to determine malicious benign samples to gain trust that in a production environment, the system is able to catch malware. With any cyber innovation brings a new set of challenges and literature soon came out about XAI as a new attack vector. Adversarial XAI (AdvXAI) is a relatively new concept but with AI applications in many sectors, it is crucial to quickly respond to the attack surface that it creates. This paper seeks to conceptualize a theoretical framework focused on addressing AdvXAI in malware analysis in an effort to balance explainability with security. Following this framework, designing a machine with an AI malware detection and analysis model will ensure that it can effectively analyze malware, explain how it came to its decision, and be built securely to avoid adversarial attacks and manipulations. The framework focuses on choosing malware datasets to train the model, choosing the AI model, choosing an XAI technique, implementing AdvXAI defensive measures, and continually evaluating the model. This framework will significantly contribute to automated malware detection and XAI efforts allowing for secure systems that are resilient to adversarial attacks.