Don't Paint It Black: White-Box Explanations for Deep Learning in Computer Security
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Deep learning is increasingly used as a basic building block of security systems. Unfortunately, deep neural networks are hard to interpret, and their decision process is opaque to the practitioner. Recent work has started to address this problem by considering black-box explanations for deep learning in computer security (CCS'18). The underlying explanation methods, however, ignore the structure of neural networks and thus omit crucial information for analyzing the decision process. In this paper, we investigate white-box explanations and systematically compare them with current black-box approaches. In an extensive evaluation with learning-based systems for malware detection and vulnerability discovery, we demonstrate that white-box explanations are more concise, sparse, complete and efficient than black-box approaches. As a consequence, we generally recommend the use of white-box explanations if access to the employed neural network is available, which usually is the case for stand-alone systems for malware detection, binary analysis, and vulnerability discovery.
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