Malware feature selection and adversarial sample generation method based on reinforcement learning

Xiaolong Li; Zhenhua Yan; Shuang Zhang; Xinghua Li; Feng Wang

doi:10.1117/12.3025806

6 May 2024 Malware feature selection and adversarial sample generation method based on reinforcement learning

Xiaolong Li, Zhenhua Yan, Shuang Zhang, Xinghua Li, Feng Wang

Proceedings Volume 13161, Fourth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2023); 131610E (2024) https://doi.org/10.1117/12.3025806
Event: Fourth International Conference on Telecommunications, Optics and Computer Science (TOCS 2023), 2023, Xi’an, China

Abstract

Amidst the intricate battleground where attackers and defenders are perpetually locked in a sophisticated digital cat-and-mouse game, the task of discerning malware within an adversarial crafted environment manifests escalating complexity. The current research delineates the utilization of an enhanced reinforcement learning technique, orchestrated to engender adversarial malware specimens, thereby strategically navigating through machine learning detectors. This endeavor not only bolsters the robustness of malware identification systems but also adeptly navigates the perpetually evolving machinations of malware creators. Within this research, an environmental model is meticulously constructed, emulating detection engines and feature extractors, with malware samples assimilated as input. By integrating an autonomously generated reward function, we ascertain the model’s agility and the concomitant generation of manifold adversarial malevolent samples. The empirical evaluations underscore that, in contrast to conventional machine learning approaches, our methodology exudes superior flexibility and efficacy, furnishing a more formidable challenge to malware detection mechanisms.

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

Citation Download Citation

Xiaolong Li, Zhenhua Yan, Shuang Zhang, Xinghua Li, and Feng Wang "Malware feature selection and adversarial sample generation method based on reinforcement learning", Proc. SPIE 13161, Fourth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2023), 131610E (6 May 2024); https://doi.org/10.1117/12.3025806

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Education and training

Adversarial training

Sensors

Machine learning

Statistical modeling

Evolutionary algorithms

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years