Black-Box Adversarial Attacks Against Deep Learning Based Malware Binaries Detection with GAN

摘要

For efficient malware detection, there are more and more deep learning methods based on raw software binaries. Recent studies show that deep learning models can easily be fooled to make a wrong decision by introducing subtle perturbations to inputs, which attracts a large influx of work in adversarial attacks. However, most of the existing attack methods are based on manual features (e.g., API calls) or in the white-box setting, making the attacks impractical in current real-world scenarios. In this work, we propose a novel attack framework called GAPGAN, which generates adversarial pay-loads (padding bytes) with generative adversarial networks (GANs). To the best of our knowledge, it is the first work that performs end-to-end black-box attacks at the byte-level against deep learning based malware binaries detection. In our attack framework, we map input discrete malware binaries to continuous space, then feed it to the generator of GAPGAN to generate adversarial payloads. We append payloads to the original binaries to craft an adversarial sample while preserving its functionality. We propose to use a dynamic threshold for reducing the loss of the effectiveness of the payloads when mapping it from continuous format back to the original discrete format. For balancing the attention of the generator to the payloads and the adversarial samples, we use an automatic weight tuning strategy. We train GAPGAN with both malicious and benign software. Once the training is finished, the generator can generate an adversarial sample with only the input malware in less than twenty milliseconds. We apply GAPGAN to attack the state-of-the-art detector MalConv and achieve 100% attack success rate with only appending payloads of 2.5% of the total length of the data for detection. We also attack deep learning models with different structures under different defense methods. The experiments show that GAPGAN outperforms other state-of-the-art attack models in efficiency and effectiveness.