Abstract
With the rapid popularity of VoIP, SPIT (Spam over Internet Telephony) based VoIP has become a security problem that cannot be ignored and SPITters (SPIT callers) detection turns into an urgent issue. Data mining is a practical method of SPITters detection. This paper considers three commonly used characteristics of VoIP users and presents the fact that the characteristic data distribution of SPITters in real data space is non-globular and irregular. Moreover, a novel approach is introduced to identify SPITters employing density-based clustering algorithm DBSCAN. The results on real dataset are superior to other commonly used unsupervised clustering algorithm in terms of the recall and precision of SPITter cluster.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
The official website of Tencent United Security Laboratory. http://slab.qq.com/news/authority/1632.html
Handley, M., Schulzrinne, H., Schooler, E., Rosenberg, J.: RFC 2543: SIP: session initiation protocol. Encycl. Internet Technol. Appl. 58(2), 1869–1877 (1999)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 96(34), 226–231 (1996)
Kentaroh, T., Iwao, S.: Unsupervised clustering-based spitters detection scheme (preprint). J. Inf. Process. 23(1), 81–92 (2015)
Wu, Y.-S., Bagchi, S., Singh, N., Wita, R.: Spam detection in voice-over-IP calls through semi-supervised clustering. In: IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2009, pp. 307–316 (2009)
Bokharaei, H.K., Sahraei, A., Ganjali, Y., Keralapura, R., Nucci, A.: You can spit, but you can’t hide: spammer identification in telephony networks. In: 2011 Proceedings IEEE INFOCOM, pp. 41–45 (2011)
Galiotos, P., Anagnostopoulos, C., Dagiuklas, T., Kotsopoulos, S.: Non-conforming behavior detection for VoIP-based network systems. In: IEEE International Conference on Communications, pp. 1–7 (2016)
Bai, Y., Su, X., Bhargava, B.: Adaptive voice spam control with user behavior analysis. In: IEEE International Conference on High PERFORMANCE Computing and Communications, pp. 354–361 (2009)
Su, M.-Y., Tsai, C.-H.: Using data mining approaches to identify voice over IP spam. Int. J. Commun Syst 28(1), 187–200 (2015)
Toyoda, K., Sasase, I.: Spit callers detection with unsupervised random forests classifier. In: IEEE International Conference on Communications, pp. 2068–2072 (2013)
Toyoda, K., Park, M., Okazaki, N., Ohtsuki, T.: Novel unsupervised spitters detection scheme by automatically solving unbalanced situation. IEEE Access 5, 6746–6756 (2017)
Sengar, H., Wang, X., Nichols, A.: Thwarting spam over internet telephony (spit) attacks on VoIP networks. In: Proceedings of the Nineteenth International Workshop on Quality of Service, p. 25. IEEE Press (2011)
Strobl, J., Mainka, B., Grutzek, G., Knospe, H.: An efficient search method for the content-based identification of telephone-spam. In: IEEE International Conference on Communications, pp. 2623–2627 (2012)
Lentzen, D., Grutzek, G., Knospe, H., Porschmann, C.: Content-based detection and prevention of spam over IP telephony - system design, prototype and first results. In: IEEE International Conference on Communications, pp. 1–5 (2011)
Balasubramaniyan, V.A., Poonawalla, A., Ahamad, M., Hunter, M.T., Traynor, P.: PinDr0p: using single-ended audio features to determine call provenance. In: ACM Conference on Computer and Communications Security, pp. 109–120 (2010)
Quittek, J., Niccolini, S., Tartarelli, S., Stiemerling, M.: Detecting spit calls by checking human communication patterns. In: IEEE International Conference on Communications, pp. 1979–1984 (2007)
Azad, M.A., Morla, R.: Mitigating spit with social strength. In: IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp. 1393–1398 (2012)
Azad, M.A., Morla, R., Arshad, J., Salah, K.: Clustering VoIP caller for spit identification. Secur. Commun. Netw. 9, 4827–4838 (2016)
Sorge, C., Seedorf, J.: A provider-level reputation system for assessing the quality of spit mitigation algorithms. In: IEEE International Conference on Communications, pp. 2282–2287 (2009)
Kaufmann, L., Rousseeuw, P.J.: Clustering by means of medoids. In: Statistical Data Analysis Based on the L1-norm and Related Methods, pp. 405–416 (1987)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Zhang, J., Wang, J., Zhang, Y., Xu, J., Wu, H. (2018). A Novel SPITters Detection Approach with Unsupervised Density-Based Clustering. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-93803-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93802-8
Online ISBN: 978-3-319-93803-5
eBook Packages: Computer ScienceComputer Science (R0)