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All issues Volume 17 (2018) ITM Web Conf., 17 (2018) 02002 References
Open Access
Issue
ITM Web Conf.
Volume 17, 2018
4th Annual International Conference on Wireless Communication and Sensor Network (WCSN 2017)
Article Number 02002
Number of page(s) 7
Section Session 2: Sensor Network
DOI https://doi.org/10.1051/itmconf/20181702002
Published online 02 February 2018
  1. Michael Shirer. Apple Tops Samsung in the Fourth Quarter to Close Out a Roller Coaster Year for the Smartphone Market[EB/OL]. http://www.idc.com/getdoc.jsp?containerId=prUS42268917. [2017-02-01]. [Google Scholar]
  2. Stat Counter Global Stats. Mobile and tablet internet usage exceeds desktop for first time worldwide[EB/OL]. http://gs.statcounter.com/press/mobile-and-tablet-internet-usage-exceeds-desktop-for-first-time-worldwide. [2016-11-01]. [Google Scholar]
  3. Wei X, Gomez L, Neamtiu I, et al. ProfileDroid: multi-layer profiling of android applications[C]//Proceedings of the 18th annual international conference on Mobile computing and networking. ACM, 2012: 137-148. [Google Scholar]
  4. Xu Q, Erman J, Gerber A, et al. Identifying diverse usage behaviors of smartphone apps[C]//Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference. ACM, 2011: 329-344. [CrossRef] [Google Scholar]
  5. Dai S, Tongaonkar A, Wang X, et al. Networkprofiler: Towards automatic fingerprinting of android apps[C]//INFOCOM, 2013 Proceedings IEEE. IEEE, 2013: 809-817. [CrossRef] [Google Scholar]
  6. Su X, Zhang D, Dai S, et al. Mobile traffic identification based on application’s network signature[J]. International Journal of Embedded Systems, 2016, 8(2/3):217. [CrossRef] [Google Scholar]
  7. Chen Z, Yu B, Zhang Y, et al. Automatic Mobile Application Traffic Identification by Convolutional Neural Networks[C]//Trustcom/BigDataSE/I SPA, 2016 IEEE. IEEE, 2016: 301-307. [CrossRef] [Google Scholar]
  8. Wang Q, Yahyavi A, Kemme B, et al. I know what you did on your smartphone: Inferring app usage over encrypted data traffic[C]//Communications and Network Security (CNS), 2015 IEEE Conference on. IEEE, 2015: 433-441. [CrossRef] [Google Scholar]
  9. Conti M, Mancini L V, Spolaor R, et al. Can’t you hear me knocking: Identification of user actions on android apps via traffic analysis[C]//Proceedings of the 5th ACM Conference on Data and Application Security and Privacy. ACM, 2015: 297-304. [Google Scholar]
  10. Park K, H. Kim Encryption Is Not Enough: Inferring user activities on KakaoTalk with traffic analysis[C]//International Workshop on Information Security Applications. Springer International Publishing, 2015: 254-265. [Google Scholar]
  11. Conti M, Mancini L V, Spolaor R, et al. Analyzing android encrypted network traffic to identify user actions[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(1): 114-125. [CrossRef] [Google Scholar]
  12. Datta J, Kataria N, Hubballi N. Network traffic classification in encrypted environment: a case study of google hangout[C]//Communications (NCC), 2015 Twenty First National Conference on. IEEE, 2015: 1-6. [Google Scholar]
  13. Saltaformaggio B, Choi H, Johnson K, et al. Eavesdropping on fine-grained user activities within smartphone apps over encrypted network traffic[C]//Proc. USENIX Workshop on Offensive Technologies (WOOT’16, in conjunction with Security’16). 2016. [Google Scholar]
  14. Fu Y, Xiong H, Lu X, et al. Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps[J]. IEEE Transactions on Mobile Computing, 2016, 15(11): 2851-2864. [CrossRef] [Google Scholar]
  15. Alan H F, J. Kaur Can Android Applications Be Identified Using Only TCP/IP Headers of Their Launch Time Traffic?[C]//Proceedings of the 9th ACM Conference on Security & Privacy in Wireless and Mobile Networks. ACM, 2016: 61-66. [CrossRef] [Google Scholar]
  16. Taylor V F, Spolaor R, Conti M, et al. Appscanner: Automatic fingerprinting of smartphone apps from encrypted network traffic[C]//Security and Privacy (EuroS&P), 2016 IEEE European Symposium on. IEEE, 2016: 439-454. [Google Scholar]
  17. Zhihua Zhou.Learning: Machine learning [M]. Tsinghua University Press, 2016. [Google Scholar]
  18. Li Deng.Analytical Depth Learning --- Speech Recognition Practice [M]. Electronic Industry Press, 2016. [Google Scholar]
  19. Müller M. Information retrieval for music and motion[M]. Heidelberg: Springer, 2007. [Google Scholar]
  20. Marco Gillies. HMMWeka[CP]. http://www.doc.gold.ac.uk/~mas02mg/software/hmmweka/index.html. [Last accessed 2017-04-20]. [Google Scholar]

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