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HFA-MD: An Efficient Hybrid Features Analysis Based Android Malware Detection Method

  • Yang Zhao
  • Guangquan Xu
  • Yao Zhang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 234)

Abstract

Lack of supervision and management of many Android third-party application markets has led to a growing number of malware on android platforms. This causes a serious privacy threat to the user’s sensitive information. To solve this problem, in this paper, a new hybrid features analysis method aiming at Android malware detection is proposed, which obtains a hybrid feature vector by extracting the information of permission requests, API calls and runtime behaviors. The characteristic of this work is the use of machine learning classification algorithms to detect malicious software. In addition, the feature selection algorithm is used to further optimize the extracted information to remove some useless features. Our experiments are based on real-world Apps, and use five different classification algorithms to detect the malware. The experiment results show that our proposed hybrid feature extraction method can improve the accuracy rate of Android malware detection compared with using static methods alone.

Keywords

Android malware detection Machine learning Static analysis Dynamic analysis Feature selection 

Notes

Acknowledgments

This work has partially been sponsored by the National Science Foundation of China (No. 61572355) and Tianjin Research Program of Application Foundation and Advanced Technology under grant No. 15JCYBJC15700, and Fundamental Research of Xinjiang Corps under grant No. 2016AC015.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.Tianjin Key Laboratory of Advanced Networking (TANK), School of Computer Science and TechnologyTianjin UniversityTianjinChina

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