NeSeDroid—Android Malware Detection Based on Network Traffic and Sensitive Resource Accessing

1. Corporation, I.D. 2015; Available from: http://www.idc.com/prodserv/smartphone-os-market-share.jsp.

2. F-Secure. 2014; Available from: https://www.f-secure.com/documents/996508/1030743/Mobile_Threat_Report_Q1_2014.pdf.

3. Sanz, B., et al., MAMA: Manifest Analysis For Malware Detection In Android. Cybern. Syst., pp. 469–488 (2013).

   Google Scholar 

4. Moonsamy, V., et al., Contrasting Permission Patterns between Clean and Malicious Android Applications, in Security and Privacy in Communication Networks, T. Zia, et al., Springer International Publishing. pp. 69–85 (2013).

   Google Scholar 

5. Gascon, H., et al., Structural detection of android malware using embedded call graphs, in Proceedings of the 2013 ACM workshop on Artificial intelligence and security. ACM: Berlin, Germany. pp. 45–54 (2013).

   Google Scholar 

6. Li, L., et al., IccTA: Detecting Inter-Component Privacy Leaks in Android Apps, in The 37th International Conference on Software Engineering (ICSE). Firenze, Italy (2015).

   Google Scholar 

7. Li, L., et al. Automatically Exploiting Potential Component Leaks in Android Applications. in Trust, Security and Privacy in Computing and Communications (TrustCom), 2014 IEEE 13th International Conference on. (2014).

   Google Scholar 

8. Aafer, Y., W. Du, and H. Yin, DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android, in Security and Privacy in Communication Networks, T. Zia, et al., Springer International Publishing. pp. 86–103 (2013).

   Google Scholar 

9. Arzt, S., 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. ACM: Edinburgh, United Kingdom. pp. 259–269 (2014).

   Google Scholar 

10. Bagheri, H., et al., COVERT: Compositional Analysis of Android Inter-App Permission Leakage. Software Engineering, IEEE Transactions on, pp. 1–1 (2015).

    Google Scholar 

11. Dini, G., et al., MADAM: a multi-level anomaly detector for android malware, in Proceedings of the 6th international conference on Mathematical Methods, Models and Architectures for Computer Network Security: computer network security. Springer-Verlag: St. Petersburg, Russia. pp. 240–253 (2012).

    Google Scholar 

12. Shabtai, A., et al., “Andromaly”: a behavioral malware detection framework for android devices. J. Intell. Inf. Syst., pp. 161–190 (2012).

    Google Scholar 

13. Zheng, C., et al., SmartDroid: an automatic system for revealing UI-based trigger conditions in android applications, in Proceedings of the second ACM workshop on Security and privacy in smartphones and mobile devices. ACM: Raleigh, North Carolina, USA. pp. 93–104 (2012).

    Google Scholar 

14. Enck, W., 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. USENIX Association: Vancouver, BC, Canada. pp. 1–6 (2010).

    Google Scholar 

15. Zaman, M., et al. Malware detection in Android by network traffic analysis. in Networking Systems and Security (NSysS) (2015).

    Google Scholar 

16. Wu, X., et al., Detect repackaged Android application based on HTTP traffic similarity. Security and Communication Networks, (2015).

    Google Scholar 

17. Zheng, M., M. Sun, and J.C.S. Lui, DroidRay: a security evaluation system for customized android firmwares, in Proceedings of the 9th ACM symposium on Information, computer and communications security. ACM: Kyoto, Japan. pp. 471–482 (2014).

    Google Scholar 

18. Feldman, S., D. Stadther, and W. Bing. Manilyzer: Automated Android Malware Detection through Manifest Analysis. in Mobile Ad Hoc and Sensor Systems (MASS), IEEE 11th International Conference on. (2014).

    Google Scholar 

19. Mobile security threat report. 2015 [cited 2015 April 10]; Available from: http://www.sophos.com/en-us/threat-center/mobile-security-threat-report.aspx.

20. Feizollah, A., et al. Comparative study of k-means and mini batch k-means clustering algorithms in android malware detection using network traffic analysis. in Biometrics and Security Technologies (ISBAST), 2014 International Symposium on. (2014).

    Google Scholar 

21. Jun, L., et al. Research of android malware detection based on network traffic monitoring. in Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on. (2014).

    Google Scholar 

22. Shabtai, A., et al., Mobile malware detection through analysis of deviations in application network behavior. Computers & Security, pp. 1–18 (2014).

    Google Scholar 

23. Malware Domain Blocklist. [cited 2015 July 10]; Available from: http://www.malwaredomains.com/?page_id=23.

24. Android Malware Genome Project 2015; Available from: http://www.malgenomeproject.org/.

25. Rasthofer, S., S. Arzt, and E. Bodden, A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks. (2014).

    Google Scholar 

26. ApkTool. 2015; Available from: https://github.com/iBotPeaches/Apktool.

27. dex2jar: Tools to work with android .dex and java .class files 2015 [cited 2015 May 20]; Available from: https://github.com/pxb1988/dex2jar.

28. WireShark. 2015 [cited 2015 May 20]; Available from: https://www.wireshark.org/.

29. Android-x86 Project - Run Android on Your PC. 2015 [cited 2015 May 10]; Available from: http://www.android-x86.org/.

30. Ubuntu 12.04.5 LTS (Precise Pangolin). 2015 [cited 2015 May 2]; Available from: http://releases.ubuntu.com/12.04/.

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