Fingerprinting Android malware families


The domination of the Android operating system in the market share of smart terminals has engendered increasing threats of malicious applications (apps). Research on Android malware detection has received considerable attention in academia and the industry. In particular, studies on malware families have been beneficial to malware detection and behavior analysis. However, identifying the characteristics of malware families and the features that can describe a particular family have been less frequently discussed in existing work. In this paper, we are motivated to explore the key features that can classify and describe the behaviors of Android malware families to enable fingerprinting the malware families with these features. We present a framework for signature-based key feature construction. In addition, we propose a frequency-based feature elimination algorithm to select the key features. Finally, we construct the fingerprints of ten malware families, including twenty key features in three categories. Results of extensive experiments using Support Vector Machine demonstrate that the malware family classification achieves an accuracy of 92% to 99%. The typical behaviors of malware families are analyzed based on the selected key features. The results demonstrate the feasibility and effectiveness of the presented algorithm and fingerprinting method.

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This work was supported in part by the Scientific Research Foundation through the Returned Overseas Chinese Scholars, Ministry of Education of China (K14C300020), in part by Shanghai Key Laboratory of Integrated Administration Technologies for Information Security (AGK2015002), in part by ZTE Corporation Foundation, and in part by the National Natural Science Foundation of China (Grant No. 61672092).

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Corresponding author

Correspondence to Wei Wang.

Additional information

Nannan Xie is currently a postdoctoral researcher in the Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, China. She received her PhD degree in computer science and technology from Jilin University, China in 2015. She has published about 20 scientific papers in various journals and international conferences. Her main research interests include network intrusion detection and mobile security.

Xing Wang is currently a PhD student in the Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, China. He received his BS degree from Beijing Jiaotong University, China in 2009. He visited King Abudullah University of Science and Technology (KAUST), Saudi Arabia from January to April 2014. His main research interests include mobile security and data processing.

Wei Wang is currently an associate professor in the Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, China. He earned his PhD degree in control science and engineering from Xi’an Jiaotong University, China in 2006. He was a postdoctoral researcher in University of Trento, Italy from 2005 to 2006. He was a postdoctoral researcher in TELECOM Bretagne and in INRIA, France from 2007 to 2008. He visited INRIA, ETH, NTNU, CNR, and New York University Polytechnic. He has authored or co-authored over 50 peer-reviewed papers in various journals and international conferences. His main research interests include mobile, computer and network security.

Jiqiang Liu received his BS (1994) and PhD (1999) degrees both from Beijing Normal University, China. He is currently a professor at the Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, China. He has published over 100 scientific papers in various journals and international conferences. His main research interests are trusted computing, cryptographic protocols, privacy preserving and network security.

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Xie, N., Wang, X., Wang, W. et al. Fingerprinting Android malware families. Front. Comput. Sci. 13, 637–646 (2019).

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  • Android malware
  • malware family
  • feature selection
  • behavior analysis