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Mobile Information Systems
Volume 2016, Article ID 6707524, 11 pages
http://dx.doi.org/10.1155/2016/6707524
Research Article

Function-Oriented Mobile Malware Analysis as First Aid

Graduate School of Information Security, Korea University, Seoul 136-713, Republic of Korea

Received 3 November 2015; Accepted 2 February 2016

Academic Editor: Seung Yang

Copyright © 2016 Jae-wook Jang and Huy Kang Kim. 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. McAfee, “McAfee Labs Threats Report,” February 2015, http://www.mcafee.com/us/resources/reports/rp-quarterly-threat-q4-2014.pdf.
  2. Apkprotect, http://www.apkprotect.com/.
  3. Bangcle, http://www.bangcle.com/.
  4. R. Yu, “Android Packer Facing the Challenges, Building Solutions,” https://www.virusbtn.com/pdf/conference_slides/2014/Yu-VB2014.pdf.
  5. 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 21th Annual Network and Distributed System Security Symposium (NDSS '14), pp. 1–15, 2014.
  6. H. Peng, C. Gates, B. Sarma et al., “Using probabilistic generative models for ranking risks of Android apps,” in Proceedings of the ACM Conference on Computer and Communications Security (CCS '12), pp. 241–252, Raleigh, NC, USA, October 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Wang, J. Zheng, C. Sun, and S. Mukkamala, “Quantitative security risk assessment of android permissions and applications,” in Data and Applications Security and Privacy XXVII, Lecture Notes in Computer Science, pp. 226–241, Springer, 2013. View at Google Scholar
  8. C. Yang, Z. Xu, G. Gu, V. Yegneswaran, and P. Porras, “DroidMiner: automated mining and characterization of fine-grained malicious behaviors in android applications,” in Computer Security—ESORICS 2014 :19th European Symposium on Research in Computer Security, Wroclaw, Poland, September 7–11, 2014. Proceedings, Part I, vol. 8712 of Lecture Notes in Computer Science, pp. 163–182, Springer, Berlin, Germany, 2014. View at Publisher · View at Google Scholar
  9. M. Zhang, Y. Duan, H. Yin, and Z. Zhao, “Semantics-aware Android malware classification using weighted contextual API dependency graphs,” in Proceedings of the 21st ACM Conference on Computer and Communications Security (CCS '14), pp. 1105–1116, ACM, Scottsdale, Ariz, USA, November 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. W. Enck, M. Ongtang, and P. McDaniel, “On lightweight mobile phone application certification,” in Proceedings of the 16th ACM Conference on Computer and Communications Security (CCS '09), pp. 235–245, ACM, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. J.-W. Jang, J. Yun, J. Woo, and H. K. Kim, “Andro-profiler: anti-malware system based on behavior profiling of mobile malware,” in Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion (WWW Companion '14), pp. 737–738, 2014.
  12. P. Pearce, A. P. Felt, G. Nunez, and D. Wagner, “AdDroid: privilege separation for applications and advertisers in Android,” in Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security (ASIACCS '12), pp. 71–72, Seoul, Republic of Korea, May 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Zhang, M. Yang, B. Xu et al., “Vetting undesirable behaviors in Android apps with permission use analysis,” in Proceedings of the ACM SIGSAC Conference on Computer & Communications Security (CCS '13), pp. 611–622, ACM, Berlin, Germany, November 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. K. Tam, S. J. Khan, A. Fattori, and L. Cavallaro, “CopperDroid: automatic reconstruction of Android malware behaviors,” in Proceedings of the 22nd Annual Network and Distributed System Security Symposium (NDSS '15), San Diego, Calif, USA, February 2015.
  15. Y. Ki, E. Kim, and H. K. Kim, “A novel approach to detect malware based on API call sequence analysis,” International Journal of Distributed Sensor Networks, vol. 2015, Article ID 659101, 9 pages, 2015. View at Publisher · View at Google Scholar
  16. D. Kim, J. Kwak, and J. Ryou, “DWroidDump: executable code extraction from android applications for malware analysis,” International Journal of Distributed Sensor Networks, vol. 2015, Article ID 379682, 9 pages, 2015. View at Publisher · View at Google Scholar
  17. Y. Zhou, Z. Wang, W. Zhou, and X. Jiang, “Hey, you, get off of my market: detecting malicious apps in official and alternative android markets,” in Proceedings of the 19th Annual Network and Distributed System Security Symposium (NDSS '12), San Diego, Calif, USA, February 2012.
  18. D.-J. Wu, C.-H. Mao, T.-E. Wei, H.-M. Lee, and K.-P. Wu, “DroidMat: android malware detection through manifest and API calls tracing,” in Proceedings of the Seventh Asia Joint Conference on Information Security (Asia JCIS '12), pp. 62–69, Tokyo, Japan, August 2012. View at Publisher · View at Google Scholar
  19. M. Zheng, M. Sun, and J. C. S. Lui, “Droid analytics: a signature based analytic system to collect, extract, analyze and associate android malware,” in Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom '13), pp. 163–171, IEEE, Melbourne, Australia, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Deshotels, V. Notani, and A. Lakhotia, “DroidLegacy: automated familial classification of Android malware,” in Proceedings of ACM SIGPLAN on Program Protection and Reverse Engineering Workshop (PPREW '14), ACM, January 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Lee, S. Lee, and H. Lee, “Screening smartphone applications using malware family signatures,” Computers & Security, 2015. View at Publisher · View at Google Scholar
  22. F-Secure, “Threat Report H1 2014,” https://www.f-secure.com/documents/996508/1030743/Threat_Report_H1_2014.pdf.
  23. F-Secure, “F-Secure, 25 Years of the Best Protection in the World,” 2013, http://www.fsecure.com/en/web/labs_global/.
  24. 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
  25. J.-W. Jang, H. Kang, J. Woo, A. Mohaisen, and H. K. Kim, “Andro-AutoPsy: anti-malware system based on similarity matching of malware and malware creator-centric information,” Digital Investigation, vol. 14, pp. 17–35, 2015. View at Publisher · View at Google Scholar
  26. L. Bergroth, H. Hakonen, and T. Raita, “A survey of longest common subsequence algorithms,” in Proceedings of the 7th International Symposium on String Processing and Information Retrieval (SPIRE '00), pp. 39–48, IEEE, A Coruña, Spain, 2000. View at Publisher · View at Google Scholar
  27. Androguard, “Reverse Engineering, Malware and Goodware Analysis of Android Applications,” 2014, https://code.google.com/p/androguard/.