Key points are not available for this paper at this time.
While deep learning has shown a great potential in various domains, the lack of transparency has limited its application in security or safety-critical areas. Existing research has attempted to develop explanation techniques to provide interpretable explanations for each classification decision. Unfortunately, current methods are optimized for non-security tasks ( e.g., image analysis). Their key assumptions are often violated in security applications, leading to a poor explanation fidelity. In this paper, we propose LEMNA, a high-fidelity explanation method dedicated for security applications. Given an input data sample, LEMNA generates a small set of interpretable features to explain how the input sample is classified. The core idea is to approximate a local area of the complex deep learning decision boundary using a simple interpretable model. The local interpretable model is specially designed to (1) handle feature dependency to better work with security applications ( e.g., binary code analysis); and (2) handle nonlinear local boundaries to boost explanation fidelity. We evaluate our system using two popular deep learning applications in security (a malware classifier, and a function start detector for binary reverse-engineering). Extensive evaluations show that LEMNA's explanation has a much higher fidelity level compared to existing methods. In addition, we demonstrate practical use cases of LEMNA to help machine learning developers to validate model behavior, troubleshoot classification errors, and automatically patch the errors of the target models.
Building similarity graph...
Analyzing shared references across papers
Loading...
Wenbo Guo
Shanghai Jiao Tong University
Dongliang Mu
Huazhong University of Science and Technology
Jun Xu
East China University of Science and Technology
Chinese Academy of Sciences
Pennsylvania State University
Virginia Tech
Building similarity graph...
Analyzing shared references across papers
Loading...
Guo et al. (Mon,) studied this question.
synapsesocial.com/papers/6a10477728c2d29469fe6614 — DOI: https://doi.org/10.1145/3243734.3243792
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: