What is it about?
The article is about using Explainable AI (XAI) to detect and analyze malware. Malware is harmful software that can damage or steal information from computers. AI has been used to identify malware, but often it works like a "black box," making decisions without showing how it arrived at them. This article reviews different XAI methods that can help us understand AI decisions in malware detection, making the process more transparent and trustworthy.
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Why is it important?
This research is crucial because it combines XAI with malware detection to make AI systems more understandable and reliable. Traditional AI methods are often opaque, leading to distrust among users. Our work not only reviews various XAI techniques but also proposes a new framework to rate the level of explainability of different models. This helps in making AI-based malware detection more transparent and resilient against attacks, providing a significant contribution to the field.
Perspectives
Malware binaries are extremely challenging to analyze because they can be very complex and varied. I feel that the current methods to measure how well we can explain AI decisions (exploitability quality assessment) are still not mature enough, especially in the field of AI. While AI can detect malware effectively, understanding the reasons behind its decisions is still a growing area. Our work aims to improve this by evaluating different XAI methods and proposing better ways to explain AI decisions in malware detection. This helps build trust in AI systems, making them more reliable and easier to use for cybersecurity professionals.
Mohd Saqib
McGill University
Read the Original
This page is a summary of: A Comprehensive Analysis of Explainable AI for Malware Hunting, ACM Computing Surveys, July 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3677374.
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