Building a Malware Detection System Based on a Machine Learning Method
Cho Do Xuan1, Tisenko Victor Nikolaevich2, Do Minh Tuan3, Nguyen The Lam4, Nguyen Anh Tuan5
1Cho Do Xuan*, FPT University, Hanoi, Vietnam.
2Tisenko Victor Nikolaevich, Peter the Great St. Petersburg Polytechnic University Russia, St. Petersburg, Polytechnicheskaya.
3Do Minh Tuan, FPT University, Hanoi, Vietnam.
4Nguyen The Lam, FPT University, Hanoi, Vietnam.
5Nguyen Anh Tuan, FPT University, Hanoi, Vietnam.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 24, 2020. | Manuscript published on March 10, 2020. | PP: 1488-1493 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2945039520/2020©BEIESP | DOI: 10.35940/ijitee.E2945.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Malware attacks are dangerous and difficult to detect and prevent. Therefore, the task of detecting signs of malware and alerting it for users or the system is very necessary today. One of the most effective malware detection approaches is applying machine learning or deep learning to analyze its behavior. There have been many studies and recommendations to analyze malicious behavior then combined with some sorting or clustering methods to find their signs. In this paper, we will propose a method to use machine learning to detect malicious signs based on their unusual behavior. Accordingly, in our research, we will conduct malicious analysis using static and dynamic analysis methods to detect abnormal behaviors and combine them with a supervised classification algorithm to the conclusion on malware behavior.
Keywords: Malware Detection, Feature Selection, Machine learning.
Scope of the Article: Machine learning.