Generating Adversarial Malware Examples with API Semantics Awareness for Black Box Attacks
As deep learning plays a key role in malware detection, it is of great practical significance to study adversarial malware examples to evaluate the robustness of malware detection algorithm based on deep learning. A black box attack is performed while malware authors are allowed only access to the input and output of the malware detection model. Due to the transferability of deep learning model, it is an effective way to train a substitute model to fit the black box model. Generative adversarial network based models show good performance in generating adversarial examples and training substitute models. However, because of the discrete output, generative adversarial networks are unable to compute gradient for their back propagation, which makes it difficult to update the weights of the neural network in the training process. In addition, APIs are important features in representing malware, but their potential semantic features are usually ignored. To address the above problems, a generative adversarial network based algorithm with API word embedding method is designed, which adopts CNN structure to train a substitute model. The substitute model is utilized to analyze the semantic association of sequential API calls. Then, we employ a long short term memory framework to generate antagonistic examples. The experimental results show that the proposed scheme is efficient and effective.
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