A near real time SMS grey traffic detection
Pages 244 - 249
Abstract
Lately, mobile operators experience threats from SMS grey routes which are used by fraudsters to evade SMS fees and to deny them millions in revenues. But more serious are the threats to the user's security and privacy and consequently the operator's reputation. Therefore, it is crucial for operators to have adequate solutions to protect both their network and their customers against this kind of fraud. Unfortunately, so far there is no sufficiently efficient countermeasure against grey routes. This paper proposes a near real time SMS grey traffic detection which makes use of Counting Bloom Filters combined with blacklist and whitelist to detect SMS grey traffic on the fly and to block them. The proposed detection has been implemented and proved to be quite efficient. The paper provides also comprehensive explanation of SMS grey routes and the challenges in their detection. The implementation and verification are also described thoroughly.
References
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3rd Generation Partnership Project; Point-to-Point Short Message Service (SMS) support on mobile radio interface. 3GPP TS 24.011
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International Telecommunication Union ITU-T: Introduction to CCITT Signalling System No. 7 Recommendation Q.700 (03/93)
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ETSI: Digital cellular telecommunications system (Phase 2); Mobile Application Part (MAP) specification (GSM 09.02 version 4.19.1) ETS 300 599
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International Telecommunication Union ITU-T: Signalling connection control part formats and codes Recommendation Q.713
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PurgeFraud.com: Bypass Fraud
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Ovum: Sustaining A2P SMS growth while securing mobile networks - Harnessing pricing elasticity to optimize enterprise messaging revenues, 2015
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Tretyakov, K: Machine Learning Techniques in Spam Filtering, Data Mining Problem-oriented Seminar, MTAT.03.177, May 2004, pp. 60--79
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Coskun, B. & Giura, P.: Mitigating SMS Spam by Online Detection of Repetitive Near-Duplicate Messages, Proceeding of 2012 IEEE International Conference on Communications (ICC)
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Jensen, Kristoffer, Do, Thanh van, Nguyen, Hai Thanh & Årnes, Andre: Better Protection of SS7 Networks With Machine Learning, Proceeding of ICITS 2016 - International Conference on IT Convergence and Security, August 2016
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Do, Thanh van et all: Detecting IMSI-Catcher Using Soft Computing, Proceeding of First International Conference on Soft Computing in Data Science, September 2015 Jaccard, P. (1901) Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 241--272
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B. H. Bloom, "Space/time trade-offs in hash coding with allowable errors," Commun. ACM, vol. 13, pp. 422--426, July 1970. [Online]. Available
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M. Ponec, P. Giura, H. Bronnimann, and J. Wein, "Highly efficient techniques for network forensics," in Proceedings of the 14th ACM conference on Computer and communications security, ser. CCS '07. New York, NY, USA: ACM, 2007, pp. 150--160. [Online]. Available: http://dl.acm.org/citation.cfm?doid=1315245.1315265
Index Terms
- A near real time SMS grey traffic detection
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Association for Computing Machinery
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Published: 26 February 2017
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ICSCA 2017
ICSCA 2017: 2017 6th International Conference on Software and Computer Applications
February 26 - 28, 2017
Bangkok, Thailand
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