Abstract: Malware detection methods combining visualization techniques and deep learning have gained widespread attention due to their high accuracy and low cost.However,deep learning models are vulnerable to adversarial attacks,where intentional small-scale perturbations can misguide the model into making incorrect decisions with high confidence.Current research on adversarial attacks targeting visualization-based detection methods for Windows malware has primarily focused on improving the effectiveness of adversarial images,while neglecting the actual harmfulness of adversarial examples.Therefore,this study proposes a novel method to generate harmful adversarial malware,BREAM(Benign-salient Region based End-to-end Adversarial Malware generation method).Firstly,selecting the salient regions of benign images as initial perturbations to enhance the attack effect on adversarial images,and introducing a mask matrix to restrict the perturbation range to ensure the functionality of adversarial examples.Then,an inverse feature mapping method is proposed to convert adversarial images into adversarial malware,achieving end-to-end generation of malware adversarial examples.The attack performance of BREAM is evaluated on four target models,and experimental results show that when the target models employ bilinear interpolation and nearest neighbor interpolation respectively,compared with existing methods,the attack success rate of adversarial images generated by BREAM has increased by an average of 47.96% and 28.39%;the attack success rate of adversarial malware has increased by an average of 53.25% and 61.93%,causing the classification accuracy of the target models to decrease by an average of 92.82% and 73.64%.

Key words: Adversarial examples,Adversarial attacks,Malware detection,Malware visualization,Convolutional neural network