Table of Contents Author Guidelines Submit a Manuscript
Security and Communication Networks
Volume 2017, Article ID 6451260, 14 pages
https://doi.org/10.1155/2017/6451260
Research Article

Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion

1College of Computer Science and Electronics Engineering, Hunan University, Changsha, China
2Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy, Changsha, China
3Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges, Hunan Police Academy, Changsha, China
4Department of Computer Sciences, New York Institute of Technology, New York, NY, USA

Correspondence should be addressed to Dafang Zhang; nc.ude.unh@gnahzfd

Received 23 January 2017; Revised 4 June 2017; Accepted 6 July 2017; Published 28 August 2017

Academic Editor: Jesús Díaz-Verdejo

Copyright © 2017 Xin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. IDC, “Apple, huawei, and xiaomi finish 2015 with above average year-over-year growth, as worldwide smartphone shipments surpass 1.4 billion for the year,” ifundefinedselectfont, 2016, http://www.idc.com/getdoc.jsp?containerId=prUS40980416.
  2. Symantec, “internet security threat report,” ifundefinedselectfont, 2016, https://www.symantec.com/content/dam/symantec/docs/reports/istr-21-2016-en.pdf.
  3. D. Arp, M. Spreitzenbarth, M. Hübner, H. Gascon, and K. Rieck, “Drebin: effective and explainable detection of android malware in your pocket,” in Proceedings of the NDSS Symposium 2014, February 2014. View at Publisher · View at Google Scholar
  4. K. Zhao, D. Zhang, X. Su, and W. Li, “Fest: a feature extraction and selection tool for Android malware detection,” in Proceedings of the 20th IEEE Symposium on Computers and Communication, (ISCC '15), pp. 714–720, July 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Du, X. Wang, and J. Wang, “A static android malicious code detection method based on multi-source fusion,” Security and Communication Networks, vol. 8, no. 17, pp. 3238–3246, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Y. Yerima, S. Sezer, and I. Muttik, “High accuracy android malware detection using ensemble learning,” IET Information Security, vol. 9, no. 6, pp. 313–320, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. W. Yang, X. Xiao, B. Andow, S. Li, T. Xie, and W. Enck, “AppContext: differentiating malicious and benign mobile app behaviors using context,” in Proceedings of the 37th IEEE/ACM International Conference on Software Engineering (ICSE '15), vol. 1, pp. 303–313, IEEE, May 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. S.-H. Seo, A. Gupta, A. M. Sallam, E. Bertino, and K. Yim, “Detecting mobile malware threats to homeland security through static analysis,” Journal of Network and Computer Applications, vol. 38, no. 1, pp. 43–53, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Kang, J.-W. Jang, A. Mohaisen, and H. K. Kim, “Detecting and classifying android malware using static analysis along with creator information,” International Journal of Distributed Sensor Networks, vol. 11, no. 6, Article ID 479174, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Arzt, S. Rasthofer, C. Fritz 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 (PLDI '14), pp. 259–269, ACM, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. V. Rastogi, Y. Chen, and W. Enck, “AppsPlayground: automatic security analysis of smartphone applications,” in Proceedings of the 3rd ACM Conference on Data and Application Security and Privacy (CODASPY '13), pp. 209–220, ACM, February 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. W. Enck, P. Gilbert, S. Han et al., “TaintDroid: an information flow tracking system for real-time privacy monitoring on smartphones,” ACM Transactions on Computer Systems, vol. 32, no. 2, article 5, 2014. View at Publisher · View at Google Scholar
  13. M. Grace, Y. Zhou, Q. Zhang, S. Zou, and X. Jiang, “RiskRanker: scalable and accurate zero-day android malware detection,” in Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys '12), pp. 281–294, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Spreitzenbarth, F. Freiling, F. Echtler, T. Schreck, and J. Hoffmann, “Mobile-sandbox: having a deeper look into Android applications,” in Proceedings of the 28th Annual ACM Symposium on Applied Computing (SAC '13), pp. 1808–1815, Association for Computing Machinery, March 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. Z. Yuan, Y. Lu, and Y. Xue, “Droiddetector: android malware characterization and detection using deep learning,” Tsinghua Science and Technology, vol. 21, no. 1, Article ID 7399288, pp. 114–123, 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Lindorfer, M. Neugschwandtner, and C. Platzer, “MARVIN: efficient and comprehensive mobile app classification through static and dynamic analysis,” Computer Software and Applications Conference, pp. 422–433, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Li, J. Ge, and G. Dai, “Detecting malware for android platform: an SVM-based approach,” in Proceedings of the 2nd IEEE International Conference on Cyber Security and Cloud Computing, pp. 464–469, November 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Y. Yerima, S. Sezer, and I. Muttik, “Android malware detection: an eigenspace analysis approach,” in Proceedings of the Science and Information Conference, (SAI '15), pp. 1236–1242, IEEE, London, UK, July 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. Wang, J. Cai, S. Cheng, and W. Li, “DroidDeepLearner: identifying android malware using deep learning,” in Proceedings of the 2016 IEEE 37th Sarnoff Symposium, pp. 160–165, Newark, NJ, USA, September 2016. View at Publisher · View at Google Scholar
  20. X. Su, D. Zhang, W. Li, and K. Zhao, “A deep learning approach to android malware feature learning and detection,” in Proceedings of the 2016 IEEE Trustcom/BigDataSE/I​SPA, pp. 244–251, Tianjin, China, August 2016. View at Publisher · View at Google Scholar
  21. X. Jiang, “Security alert: new droidkungfu variant,” ifundefinedselectfont, 2011, https://www.csc.ncsu.edu/faculty/jiang/DroidKungFu3/.
  22. B. Sarma, N. Li, C. Gates, R. Potharaju, C. Nita-Rotaru, and I. Molloy, “Android permissions: a perspective combining risks and benefits,” in Proceedings of the 17th ACM Symposium on Access Control Models and Technologies, pp. 13–22, ACM, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. K. A. Talha, D. I. Alper, and C. Aydin, “APK Auditor: permission-based android malware detection system,” Digital Investigation, vol. 13, pp. 1–14, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. D. Ferreira, V. Kostakos, A. R. Beresford, J. Lindqvist, and A. K. Dey, “Securacy: an empirical investigation of android applications' network usage, privacy and security,” in Proceedings of the 8th ACM Conference on Security and Privacy in Wireless and Mobile Networks, (WiSec '15), June 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. Android, “basebridge,” ifundefinedselectfont, 2011, http://www.symantec.com/securityresponse/writeup.jsp?docid=2011-060915-4938-99&tabid=2.
  26. Apktool, ifundefinedselectfont, http://code.google.com/p/android-apktool/.
  27. M. Z. Mas'Ud, S. Sahib, M. F. Abdollah, S. R. Selamat, and R. Yusof, “Analysis of features selection and machine learning classifier in android malware detection,” in Proceedings of the 5th International Conference on Information Science and Applications, ICISA 2014, pp. 1–5, IEEE, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. J. T. Kent, “Information gain and a general measure of correlation,” Biometrika, vol. 70, no. 1, pp. 163–173, 1983. View at Publisher · View at Google Scholar · View at Scopus
  29. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, Berlin, Germany, 2001. View at Publisher · View at Google Scholar · View at MathSciNet
  30. R. K. Shahzad and N. Lavesson, “Comparative analysis of voting schemes for ensemble-based malware detection,” Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 4, no. 1, pp. 98–117, 2013. View at Google Scholar · View at Scopus
  31. G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, USA, 1976. View at MathSciNet
  32. A. P. Dempster, “Upper and lower probabilities induced by a multivalued mapping,” Annals of Mathematical Statistics, vol. 38, pp. 325–339, 1967. View at Publisher · View at Google Scholar · View at MathSciNet
  33. Y. Zhou and X. Jiang, “Dissecting android malware: characterization and evolution,” in Proceedings of the 33rd IEEE Symposium on Security and Privacy, pp. 95–109, San Francisco, Calif, USA, May 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. V. Total, ifundefinedselectfont, 2013, https://www.virustotal.com/en/.