MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||8|
|Section||Network Security and Software Design|
|Published online||04 March 2020|
Packed malware variants detection using deep belief networks
1 Strategic Support Force Information Engineering University, Zhengzhou, 450000, China
2 DaTang Centrol-China Electric Power Test Research Institute, Zhengzhou, 450006, China
* Corresponding author: firstname.lastname@example.org
Malware is one of the most serious network security threats. To detect unknown variants of malware, many researches have proposed various methods of malware detection based on machine learning in recent years. However, modern malware is often protected by software packers, obfuscation, and other technologies, which bring challenges to malware analysis and detection. In this paper, we propose a system call based malware detection technology. By comparing malware and benign software in a sandbox environment, a sensitive system call context is extracted based on information gain, which reduces obfuscation caused by a normal system call. By using the deep belief network, we train a malware detection model with sensitive system call context to improve the detection accuracy.
Key words: Malware / Deep belief network / Sensitive system call / Information gain
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.