ABSTRACT
Smart spammers and telemarketers circumvent the standalone spam detection systems by making low rate spam-ming activity to a large number of recipients distributed across many telecommunication operators. The collaboration among multiple telecommunication operators (OPs) will allow operators to get rid of unwanted callers at the early stage of their spamming activity. The challenge in the design of collaborative spam detection system is that OPs are not willing to share certain information about behaviour of their users/customers because of privacy concerns. Ideally, operators agree to share certain aggregated statistical information if collaboration process ensures complete privacy protection of users and their network data. To address this challenge and convince OPs for the collaboration, this paper proposes a decentralized reputation aggregation protocol that enables OPs to take part in a collaboration process without use of a trusted third party centralized system and without developing a predefined trust relationship with other OPs. To this extent, the collaboration among operators is achieved through the exchange of cryptographic reputation scores among OPs thus fully protects relationship network and reputation scores of users even in the presence of colluders. We evaluate the performance of proposed protocol over the simulated data consisting of five collaborators. Experimental results revealed that proposed approach outperforms standalone systems in terms of true positive rate and false positive rate.
- (2016, 01, December) Communications Fraud Control association (CFCA) Announces Results of Worldwide Telecom Fraud Survey published in 2016. {Online}. Available: http://cfca.org/fraudlosssurvey/2015.pdfGoogle Scholar
- H. Tu, A. Doupé, Z. Zhao, and G. Ahn, "SoK: Everyone Hates Robocalls: A Survey of Techniques against Telephone Spam," in 37th IEEE Symposium on Security and Privacy, 2016. Google ScholarCross Ref
- C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Y. Zhu, "Tools for privacy preserving distributed data mining," SIGKDD Explor. Newsl, vol. 4, no. 2, pp. 28--34, Dec. 2002. Google ScholarDigital Library
- E. Pavlov, J. S. Rosenschein, and Z. Topol, Supporting Privacy in Decentralized Additive Reputation Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, pp. 108--119. Google ScholarCross Ref
- Y. Hong, S. Kunwadee, Z. Hui, S. ZonYin, and S. Debanjan, "Incorporating Active Fingerprinting into SPIT Prevention Systems," in The 3rd Annual VoIP Security Workshop, 2006.Google Scholar
- D. Lentzen, G. Grutzek, H. Knospe, and C. Porschmann, "Content-Based Detection and Prevention of Spam over IP Telephony - System Design, Prototype and First Results," in IEEEICC2011, Japan, 2011, pp. 1--5.Google Scholar
- M. Hansen, M. Hansen, J. Möller, T. Rohwer, C. Tolkmit, and H. Waack, "Developing a Legally Compliant Reachability Management System Countermeasure against SPIT," in Third Annual VoIP Security Workshop, Berlin, Germany, 2006.Google Scholar
- V. Balasubramaniyan, M. Ahamad, and H. Park, "CallRank: Combating SPIT Using Call Duration, Social Networks and Global Reputation," in Fourth CEAS2007., 2007.Google Scholar
- Y.-S. Wu, S. Bagchi, N. Singh, and R. Wita, "Spam Detection in Voice- Over-IP Calls through Semi-Supervised Clustering," in 39th Annual IEEE/IFIP DSN, Portugal, 2009, pp. 307--316.Google Scholar
- P. Kolan and R. Dantu, "Socio-Technical Defense Against Voice Spamming," ACM Trans. Auton. Adapt. Syst., vol. 2, no. 1, 2007. Google ScholarDigital Library
- H. Bokharaei, A. Sahraei, Y. Ganjali, R. Keralapura, and A. Nucci, "You can SPIT, but You can't hide: Spammer Identification in Telephony Networks," in 2011 IEEE INFOCOM, 2011, pp. 41--45.Google Scholar
- R. Zhang and A. Gurtov, "Collaborative reputation-based voice spam filtering," in DEXA '09., 2009, pp. 33--37. Google ScholarDigital Library
- J. Quittek, S. Niccolini, S. Tartarelli, M. Stiemerling, M. Brunner, and T. Ewald, "Detecting SPIT Calls by Checking Human Communication Patterns," in IEEE ICC, Scotland, 2007, pp. 1979--1984.Google Scholar
- P. Gupta, B. V. Srinivasan, B, and M. Ahamad, "Phoneypot: Data-driven Understanding of Telephony Threats," in 20th NDSS, 2015.Google Scholar
- M. Balduzzi, P. Gupta, L. Gu, Gao. D, and M. Ahamad, "MobiPot: Understanding Mobile Telephony Threats with Honeycards," in In 11th ACM ASIACCS, 2016. Google ScholarDigital Library
- M. Azad and R. Moria, "Multistage SPIT Detection in Transit VoIP," in 19 IEEE SoftCOM, 2011, pp. 1--9.Google Scholar
- B. Mathieu, S. Niccolini, and D. Sisalem, "SDRS: A Voice-over-IP Spam Detection and Reaction System," IEEE Security and Privacy, vol. 6, pp. 52--59, 2008. Google ScholarDigital Library
- C. Sorge and J. Seedorf, "A Provider-Level Reputation System for Assessing the Quality of SPIT Mitigation Algorithms," in IEEE ICC '09., 2009, pp. 1--6. Google ScholarDigital Library
- F. Wang, Y. Mo, and B. Huang, "P2p-avs: P2p based cooperative voip spam filtering," in IEEE 2007 WCNC., 2007, pp. 3547--3552. Google ScholarDigital Library
- E. Damiani, S. De Capitani di Vimercati, S. Paraboschi, and P. Samarati, "P2p-based collaborative spam detection and filtering," in Fourth International Conference on P2P Computing, 2004, pp. 176--183. Google ScholarDigital Library
- K. Li, Z. Zhong, and L. Ramaswamy, "Privacy-aware collaborative spam filtering," IEEE Trans. Parallel Distrib. Syst., vol. 20, no. 5, pp. 725--739, 2009. Google ScholarDigital Library
- M. Sirivianos, K. Kim, and X. Yang, "Socialfilter: introducing social trust to collaborative spam mitigation," in 2010 CollSec., 2010, pp. 7--7. Google ScholarDigital Library
- Distributed checksum clearinghouses. {Online}. Available: http://www.rhyolite.com/dcc/Google Scholar
- O. Hasan, L. Brunie, and E. Bertino, "Preserving privacy of feedback providers in decentralized reputation systems," Computers & Security, vol. 31, no. 7, pp. 816 -- 826, 2012. Google ScholarDigital Library
- M. Seshadri, S. Machiraju, A. Sridharan, J. Bolot, C. Faloutsos, and J. Leskove, "Mobile call graphs: beyond power-law and lognormal distributions," in 14th ACM SIGKDD, 2008, pp. 596--604. Google ScholarDigital Library
- S. Chiappetta, C. Mazzariello, R. Presta, and S. Romano, "An anomaly-based approach to the analysis of the social behavior of voip users," Computer Networks, vol. 57, no. 6, pp. 1545 -- 1559, 2013. Google ScholarDigital Library
- N. dHeureuse, S. Tartarelli, and S. Niccolini, "Analyzing Telemarketer Behavior in Massive Telecom Data Records," in Springer Trustworthy Internet, 2011, pp. 261--271.Google Scholar
- M. A. Azad and R. Morla, "Caller-Rep: Detecting unwanted calls with caller social strength," Computers & Security, vol. 39, Part B, pp. 219--236, 2013. Google ScholarDigital Library
- D. Boneh and A. Silverberg, "Applications of multilinear forms to cryptography," Contemporary Mathematics, vol. 324, no. 1, pp. 71--90, 2003. Google ScholarCross Ref
- F. Hao, P. Y. A. Ryan, and P. Zielinski, "Anonymous voting by two-round public discussion," IET Information Security, vol. 4, no. 2, pp. 62--67, June 2010. Google ScholarCross Ref
- A. A. Nanavati, S. Gurumurthy, G. Das, D. Chakraborty, K. Dasgupta, S. Mukherjea, and A. Joshi, "On the Structural Properties of Massive Telecom Call Graphs: Findings and Implications," in 15th ACM CIKM '06, 2006, pp. 435--444. Google ScholarDigital Library
Index Terms
- Decentralized privacy-aware collaborative filtering of smart spammers in a telecommunication network
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