Universal adversarial triggers for attacking against API sequence: based malware detector
Mengying Xiong, Chunyang Ye · 2024
This paper explores adversarial machine learning attacks in the context of malware detection, focusing on API sequencebased models. The vulnerability of machine learning algorithms to well-crafted attacks is addressed, particularly in the non-invertible and non-differentiable software domain. A preprocessing method is proposed to tackle issues of imbalance and excessive length in API sequences, enhancing model accuracy and reducing training time. Additionally, a universal trigger attack method for API sequence-based malware detection is introduced. This approach demonstrates transferable adversarial triggers, enabling black-box attacks without prior knowledge of the target model. Experimental results validate the effectiveness of the strategy, particularly in reducing attack overhead for deep learning models. Specifically, the average attack effectiveness in the problem space is 86.68%, with an average attack overhead of 0.0020%. Overall, our work contributes to advancing the understanding and mitigation of adversarial attacks in API sequence-based malware detection