Universal adversarial triggers for attacking against API sequence: based malware detector

Mengying Xiong; Chunyang Ye

doi:10.1117/12.3027134

1 April 2024 Universal adversarial triggers for attacking against API sequence: based malware detector

Mengying Xiong, Chunyang Ye

Proceedings Volume 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024); 130770K (2024) https://doi.org/10.1117/12.3027134
Event: 4th International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 2024, Chicago, IL, United States

Abstract

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

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mengying Xiong and Chunyang Ye "Universal adversarial triggers for attacking against API sequence: based malware detector", Proc. SPIE 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 130770K (1 April 2024); https://doi.org/10.1117/12.3027134

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available