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Android Malware Detection and Malware Behavior Analysis Based on Machine Learning

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DOI: 10.23977/icamcs2019.38

Author(s)

Jingya Liu

Corresponding Author

Jingya Liu

ABSTRACT

With the rapid development of mobile Internet, Android system occupies most of the market share of mobile platforms, while the number of Android applications released is also on an explosive growth. Accurate and efficient Android malware detection technology is not only the urgent need of users for their own security, but also the premise of Android market development. The openness of Android platform and the lack of relevant regulatory standards make it a hotbed for malware. However, the research on detection and defense of Android malware is still in the initial stage. Thanks to the enhancement of data processing ability in the cloud, it has become a trend to apply data mining, machine learning and other classic theories and tools to the field of mobile terminal malware detection. The purpose of this paper is to study the Android malware detection based on machine learning classification algorithm, to realize the security model of static detection and dynamic detection, terminal and cloud, detection and control.

KEYWORDS

Android, Malicious software, Testing, Malicious act

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