A Dynamic Analysis-Powered Explanation Framework for Malware Detection

Deep learning has been widely adopted in Android malicious software (malware) detection. However, poor explanation in deep learning-based detection models severely undermines user trusts and poses a significant obstacle to their practical promotion in critical security domains. Some studies strive to uncover the rationale behind a model's decision. Unfortunately, these efforts are often hindered by the limitations of feature extraction methods, such as primarily relying on static analysis to derive separate and approximate behavioral descriptions of applications (apps). As a result, establishing a reliable interpretation for deep learning-based malware detection models remains an open issue. In this work, we propose a novel framework XDeepMal to interpret deep learning-based malware detection models. Specifically, in XDeepMal, we formulate a dynamic analysis tool XTracer <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> to capture runtime behaviors of apps and automatically generate their continuous behavior trajectories. Then, we propose a novel interpreter to pinpoint certainty behavior fragments that are crucial for deep learning models to make their decisions. This approach regards the identification of the most critical fragments as an optimization problem and leverages heuristic algorithms for implementation. We conduct extensive experiments on a real-world dataset to investigate the effectiveness and reliability of XDeepMal. These experiments cover intuitive case studies (malware family and individual app) and in-depth quantitative analysis. Additionally, we evaluate its coverage and efficiency. Our experimental results demonstrate that XDeepMal is capable of generating convincing interpretations for deep learning (e.g., Transformer) based models within feasible inference time, which greatly benefits security analysts in accurately comprehending why an app is identified as malware by deep learning-based detection models.