A Multi-Strategy Adversarial Attack Method for Deep Learning Based Malware Detectors

Deep learning allows building high-accuracy malware detectors without complicated feature engineering. However, research shows that the deep learning model is vulnerable and can be deceived if attackers add perturbation to input samples to craft adversarial examples deliberately. By altering the pixel values of the images, attackers have been able to generate adversarial examples that can fool state-of-the-art deep learning based image classifiers. However, Windows malware is a structured binary program file. Therefore, arbitrarily altering its contents will often break the program's functionality. In order to solve this problem, a standard but inefficient method is to run the sample in the sandbox to verify whether its functionality is preserved. This paper proposes a multi-strategy adversarial attack method, which can generate malware adversarial examples with functionality-preserving. Our method manipulates the redundant or extended space in the Windows malware binary, so it will not break functionality. Experiments show that our method has a high attack success rate and efficiency.