Abstract
Cyber wars and cyber attacks are a serious problem in the global digital environment. Technological progress is forcing malware authors to create more and more advanced and sophisticated malware. Such programs are almost impossible to detect with static analysis. Even when using dynamic analysis, a malicious file can recognize being executed by the virtual environment and change its code. Therefore, this study aims to create a dynamic analysis system, where the executable file is not able to detect being observed and can show its proposed in this article and fed to a machine learning algorithm - a support vector machine.
Publisher
National Academy of Sciences of Ukraine (Co. LTD Ukrinformnauka) (Publications)
Reference12 articles.
1. 1. Tamas K Lengyel, Steve Maresca, Bryan D Payne, George D Webster, Sebastian Vogl, and Aggelos Kiayias. Scalability, fidelity and stealth in the drakvuf dynamic malware analysis system. In The 30th Annual Computer Security Applications Conference, pages 386-395, 2014
2. 2. Xen Project. Available at: xenproject.org
3. 3. LibVMI. Available at: libvmi.com
4. 4. Muhammad Ijaz, Muhammad Hanif Durad, Maliha Ismail, Static and Dynamic Malware Analysis Using Machine Learning, 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST - 2019), January 2019
5. 5. Matthew Nunes, Pete Burnap, Omer F. Rana, Getting to the root of the problem: A detailed comparison of kernel and user level data for dynamic malware analysis, Journal of Information Security and Applications, October 2019