SCIENCE CHINA Information Sciences, Volume 63 , Issue 3 : 139103(2020) https://doi.org/10.1007/s11432-018-9615-8

Hybrid malware detection approach with feedback-directed machine learning

More info
  • ReceivedJul 20, 2018
  • AcceptedSep 25, 2018
  • PublishedFeb 11, 2020


There is no abstract available for this article.


This work was supported in part by National Key Research and Development Program of China (Grant No. 2018YFB1003702), National Natural Science Foundation of China (Grant No. 61872274), and Hunan Provincial Natural Science Foundation of China for Distinguished Young Scholars (Grant No. 2018JJ1025).


[1] DATA G. Threat situation for mobile devices worsens. https://www.gdatasoftware.com/news/2017/02/threat-situation-for-mobile-devices-worsens. Google Scholar

[2] Enck W, Ongtang M, McDaniel P. On lightweight mobile phone application certification. In: Proceedings of the 16th ACM conference on Computer and communications security, Chicago, 2009. 235--245. Google Scholar

[3] Shabtai A, Fledel Y, Elovici Y. Automated static code analysis for classifying android applications using machine learning. In: Proceedings of International Conference on Computational Intelligence and Security, Nanning, 2010. 329--333. Google Scholar

[4] Arzt S, Rasthofer S, Fritz C, et al. FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps. In: Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation, New York, 2014. 259--269. Google Scholar

[5] Enck W, Gilbert P, Chun B G, et al. TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones. In: Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation (OSDI'10),Berkeley, 2010. 393--407. Google Scholar

[6] Burguera I, Zurutuza U, Nadjm-Tehrani S. Crowdroid: behavior-based malware detection system for Android. In: Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices (SPSM '11), New York, 2011. 15--26. Google Scholar

[7] Islam R, Tian R, Batten L M. Classification of malware based on integrated static and dynamic features. J Network Comput Appl, 2013, 36: 646-656 CrossRef Google Scholar

[8] Shi Y, You W Q, Qian K, et al. A hybrid analysis for mobile security threat detection. In: Proceedings of the 7th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, 2016. Google Scholar

[9] Wu D J, Mao C H, Wei T E, et al. DroidMat: Android malware detection through manifest and API calls tracing. In: Proceedings of the 7th Asia Joint Conference on Information Security, Tokyo, 2012. 62--69. Google Scholar