Abstract
With the rapid development of the Internet, the scale of Internet users has been expanding. At the same time, various mobile phone applications have emerged in an endless stream. While providing users with a variety of services, it also leads to the difficulty of user identification. All those things bring new challenges to traffic identification. Based on the importance of Internet traffic identification for Internet management and security, this paper describes the classification methods and problems faced by Internet traffic identification. Moreover, current work about user-related and application-related traffic identification methods is summarized and analyzed. At the end of this article, we will discuss the future research prospects of traffic identification and summarize the content of the article.
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References
FBI: 2017 Internet Crime Report. https://pdf.ic3.gov/2017_IC3Report.pdf. Accessed 10 Nov 2018
Internet Live Stats. http://www.internetlivestats.com/internet-users. Accessed 10 Nov 2018
Claffy, K.C., Braun, H.W., Polyzos, G.C.: A parameterizable methodology for Internet traffic flow profiling. IEEE J. Sel. Areas Commun. 13(8), 1481–1494 (1995)
IANA Port Number List. https://www.iana.org/assignments/service-names-port-numbers/service-names-port-numbers.xml. Accessed 10 Nov 2018
Husák, M., Čermák, M., Jirsík, T., et al.: HTTPS traffic analysis and client identification using passive SSL/TLS fingerprinting. EURASIP J. Inf. Secur. 2016(1), 6 (2016)
Celeda, P.: Network-based HTTPS client identification using SSL/TLS fingerprinting. In: International Conference on Availability, Reliability and Security, pp. 389–396. IEEE Computer Society (2015)
Masud, M.M., Al-Khateeb, T., Khan, L., et al.: Flow-based identification of botnet traffic by mining multiple log files. In: International Conference on Distributed Framework and Applications, pp. 200–206. IEEE (2009)
Ibrahim, H.A.H., Nor, S.M., Jamil, H.A.: Online hybrid internet traffic classification algorithm based on signature statistical and port methods to identify internet applications. In: IEEE International Conference on Control System, Computing and Engineering, pp. 185–190. IEEE (2014)
Bolzoni, D., Etalle, S., Hartel, P., et al.: POSEIDON: a 2-tier anomaly-based network intrusion detection system. In: IEEE International Workshop on Information Assurance, 10 pp.-156
Dharmapurikar, S., Krishnamurthy, P., Sproull, T.S., et al.: Deep packet inspection using parallel bloom filters. IEEE Micro 24(1), 52–61 (2004)
Sherry, J., Lan, C., Popa, R.A., et al.: BlindBox: deep packet inspection over encrypted traffic. ACM SIGCOMM Comput. Commun. Rev. 45(5), 213–226 (2015)
Ise I.: Specification of the IP flow information export (IPFIX) protocol for the exchange of IP traffic flow information RFC 5101 (2008)
Liu, J., Liu, F., He, D.: The identification for P2P thunder traffic based on deep flow identification. In: International Conference on Cloud Computing and Intelligent Systems, pp. 504–507. IEEE (2013)
Meidan, Y., Bohadana, M., Shabtai, A., et al.: ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis. In: Symposium on Applied Computing, pp. 506–509. ACM (2017)
Zander, S., Nguyen, T., et al.: Automated traffic classification and application identification using machine learning. In: IEEE Conference on Local Computer Networks, Anniversary, pp. 250–257. IEEE (2005)
Clarke, N., Li, F., Furnell, S.: A novel privacy preserving user identification approach for network traffic. Comput. Secur. 70, 335–350 (2017)
Yoon, S.H., Park, J.S., Kim, M.S., et al.: Behavior signature for big data traffic identification. In: International Conference on Big Data and Smart Computing, pp. 261–266. IEEE (2014)
Shim, K.S., Yoon, S.H., Sija, B.D., et al.: Effective behavior signature extraction method using sequence pattern algorithm for traffic identification. Int. J. Netw. Manag. 28(5), e2011 (2017)
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. In: ACM Conference on Data and Application Security and Privacy, pp. 297–304. ACM (2015)
Jaiswal, R.C., Lokhande, S.D.: Machine learning based internet traffic recognition with statistical approach (2013)
Zander, S., Nguyen, T.T.T., Armitage, G.J.: Automated traffic classification and application identification using machine learning. In: IEEE Conference on Local Computer Networks. IEEE (2005)
Crotti, M., Dusi, M., Gringoli, F., et al.: Traffic classification through simple statistical fingerprinting. ACM SIGCOMM Comput. Commun. Rev. 37(1), 5 (2007)
Zhang, J., Chen, C., Xiang, Y., et al.: Robust network traffic identification with unknown applications. In: ACM SIGSAC Symposium on Information. ACM (2013)
Gu, X., Yang, M., Fei, J., et al.: A novel behavior-based tracking attack for user identification. In: Third International Conference on Advanced Cloud & Big Data. IEEE (2016)
Shi, J., Zhang, Z., Li, Y., Wang, R., Shi, H., Li, X.: New method for computer identification through electromagnetic radiation. CMC: Comput. Mater. Continua 57(1), 69–80 (2018)
Liu, Y., Peng, H., Wang, J.: Verifiable diversity ranking search over encrypted outsourced data. CMC: Comput. Mater. Continua 55(1), 037–057 (2018)
Acknowledgement
This work was supported by National Key Research & Development Plan of China under Grant 2016QY05X1000, National Natural Science Foundation of China under Grant No. 61771166, CERNET Innovation Project under Grant No. NGII20170101, and Dongguan Innovative Research Team Program under Grant No. 201636000100038.
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Zhu, Q., Li, D., Xin, Y., Yu, X., Mu, G. (2019). A Survey on Network Traffic Identification. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11635. Springer, Cham. https://doi.org/10.1007/978-3-030-24268-8_9
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DOI: https://doi.org/10.1007/978-3-030-24268-8_9
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