Detecting Malicious Applications on Android is based on Static Analysis using Machine Learning Algorithm
Tisenko Victor Nikolaevich1, Lai Van Duong2, Ha Tuan Anh3, Nguyen Quang Dam4, Nguyen Quoc Hoang5
1Tisenko Victor Nikolaevich*, Department Quality Systems, Peter the Great St. Petersburg Polytechnic University, Russia.
2Lai Van Duong, Faculty of Information Technology, FPT University, Hanoi, Vietnam.
3Ha Tuan Anh, Faculty of Information Technology, FPT University, Hanoi, Vietnam.
4Nguyen Quang Dam, Faculty of Information Technology, FPT University, Hanoi, Vietnam.
5Nguyen Quoc Hoang, Faculty of Information Technology, FPT University, Hanoi, Vietnam.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 30, 2020. | Manuscript published on April 10, 2020. | PP: 1283-1287 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3631049620/2020©BEIESP | DOI: 10.35940/ijitee.F3631.049620
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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: Attacks on users through mobile devices in general, and mobile devices with Android operating system in particular, have been causing many serious consequences. Research  lists the vulnerabilities found in the Android operating system, making it the preferred target of cyberattackers. Report  statistics the number of cyberattacks via mobile devices and mobile devices using Android operating system. The report points out the insecurity of information from applications downloaded by users from Android apps stores. Therefore, to prevent the attack and distribution of malware through Android apps, it is necessary to research the method of detecting malicious code from the time users download applications to their devices. Recent approaches often rely on static analysis and dynamic analysis to look for unusual behavior in applications. In this paper, we will propose the use of static analysis techniques to build a behavior of malicious code in the application and machine learning algorithms to detect malicious behavior.
Keywords: Malicious Applications on Android, static Analysis, Abnormal Behavior, Machine Learning.
Scope of the Article: Machine Learning.