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Explainable AI for Malware Hunting
We have tremendous examples where AI is effectively applied to real-world data, especially with images, natural language processing (NLP), and other multimedia-related tasks. However, when it comes to binary data, the field remains largely unexplored. In this article, we’ll dive into the potential of Explainable AI (XAI) for analyzing malicious binaries, opening doors to insights and breakthroughs in cybersecurity with the help of this article.
Typically, in malware analysis, we work with binary files because source code is often unavailable. Most XAI algorithms are designed for NLP or image data, not for binary data. Even if we adapt these algorithms to highlight a malicious binary string within a large binary, it often doesn’t provide meaningful insights.
So, we need to customize our explainability methods to provide meaningful clues to malware analysts and reduce their workload. Malware analysis using XAI can generally be broken down into static, dynamic, and hybrid approaches.
