XAI in cybersecurity: A survey on techniques, challenges, and future directions

This paper surveys the application of explainable artificial intelligence (XAI) in cybersecurity, including malware detection, intrusion detection, and adversarial attacks. Without a doubt, artificial intelligence (AI) has made its way to decision-making in these applications. The primary focus has been on building AI models that are both accurate and efficient. However, cyber analysts and security experts must understand the underlying reasons behind security decisions to resolve existing issues and improve system security. The black-box nature of AI models makes it difficult to provide such an understanding, leading to a lack of AI robustness and its widespread adoption across industries. On the other hand, XAI is a collection of AI models that enhance AI robustness, thereby having the potential to address the aforementioned challenges. The primary objective of this paper is to discuss AI cybersecurity challenges and how XAI can be utilized to address these challenges, provide an overview of XAI techniques, and present and compare state-of-the-art research in XAI for cybersecurity. The paper also addresses the weaknesses of XAI techniques related to their security vulnerabilities and resiliency to adversarial attacks. Finally, challenges associated with using XAI for cybersecurity are also discussed to spur further research.