Adversarial Example Attacks Against Intelligent Malware Detection: A Survey

• Published in: 2022 4th International Conference on Applied Machine Learning (ICAML) pp. 1 - 7
• Main Authors: Qi, Xuyan, Tang, Yonghe, Wang, Huanwei, Liu, Tieming, Jing, Jing
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2022
• Subjects: adversarial attacks; adversarial example; Computer vision; Deep learning; Detectors; intelligent malware detection; malware; Perturbation methods; Predictive models; Privacy; Technological innovation
• DOI: 10.1109/ICAML57167.2022.00068

With the advancement of information technology and the increasing prevalence of the internet, the number of malware has experienced an exponential, and the security threat to users' property and privacy is becoming more serious. With the great success of artificial intelligence especially deep learning technology in fields such as computer vision and speech processing, deep learning are gradually introduced into the field of malware detection, to process large amounts of malware more efficiently. However, in recent years, researchers have discovered that the deep learning model itself also has security risks. While malware detection based on deep learning brings convenience and technical progress, it also introduces some new threats, such as adversarial examples. This paper investigates adversarial example attacks against deep learning-based malware detectors. First, We reviewed research on malware detection techniques. Then we analyze the works related to adversarial example attacks against intelligent malware detectors. Finally, we discuss the challenges and prospects in future research.