Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection
This addresses the robustness of malware detectors for security applications, offering a black-box, interpretable method that is incremental over prior adversarial attack techniques.
The paper tackles the problem of generating adversarial examples for deep neural networks in malware detection without requiring model details or many samples, achieving a success rate of nearly 100% in fooling DNNs in certain cases.
Deep neural networks (DNNs) are increasingly being applied in malware detection and their robustness has been widely debated. Traditionally an adversarial example generation scheme relies on either detailed model information (gradient-based methods) or lots of samples to train a surrogate model, neither of which are available in most scenarios. We propose the notion of the instance-based attack. Our scheme is interpretable and can work in a black-box environment. Given a specific binary example and a malware classifier, we use the data augmentation strategies to produce enough data from which we can train a simple interpretable model. We explain the detection model by displaying the weight of different parts of the specific binary. By analyzing the explanations, we found that the data subsections play an important role in Windows PE malware detection. We proposed a new function preserving transformation algorithm that can be applied to data subsections. By employing the binary-diversification techniques that we proposed, we eliminated the influence of the most weighted part to generate adversarial examples. Our algorithm can fool the DNNs in certain cases with a success rate of nearly 100\%. Our method outperforms the state-of-the-art method . The most important aspect is that our method operates in black-box settings and the results can be validated with domain knowledge. Our analysis model can assist people in improving the robustness of malware detectors.