Netinfo Security ›› 2020, Vol. 20 ›› Issue (1): 67-74.doi: 10.3969/j.issn.1671-1122.2020.01.010

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Multi-feature Android Malware Detection Method

HOU Liuyang, LUO Senlin(), PAN Limin, ZHANG Ji   

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
  • Received:2019-09-10 Online:2020-01-10 Published:2020-05-11

Abstract:

Aiming at the current problem that the feature construction of Android malware detection method based on machine learning has a single dimension and it is difficult to comprehensively characterize the behavior characteristics of Android malware, this paper proposes a malicious software detection method that integrates the behavior characteristics of software, the structural characteristics of AndroidManifest.xml file and the characteristics of Android malware analysis experience. This method extracts the N-gram semantic information, system sensitive API, system Intent, system Category, sensitive authority and relevant experience characteristics of the Dalvik operand code of Android application, characterizes the behavior of Android malware in multiple directions, and constructs the feature vector. Then, the integrated learning algorithm based on XGBoost is used to construct the classification model, so as to realize the accurate classification of malware. Experiments were conducted on DREBIN and AMD in the open data set, and the experimental results showed that this method could achieve a detection accuracy of over 97%, which effectively improved the detection effect of Android malware.

Key words: Android, malware, multi-feature, XGBoost

CLC Number: