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Lookup NU author(s): Dr Muhammad Azad
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© 2016 John Wiley & Sons, Ltd. The number of unsolicited and advertisement telephony calls over traditional and Internet telephony has rapidly increased over recent few years. Every year, the telecommunication regulators, law enforcement agencies and telecommunication operators receive a very large number of complaints against these unsolicited, unwanted calls. These unwanted calls not only bring financial loss to the users of the telephony but also annoy them with unwanted ringing alerts. Therefore, it is important for the operators to block telephony spammers at the edge of the network so to gain trust of their customers. In this paper, we propose a novel spam detection system by incorporating different social network features for combating unwanted callers at the edge of the network. To this extent the reputation of each caller is computed by processing call detailed records of user using three social network features that are the frequency of the calls between caller and the callee, the duration between caller and the callee and the number of outgoing partners associated with the caller. Once the reputation of the caller is computed, the caller is then places in a spam and non-spam clusters using unsupervised machine learning. The performance of the proposed approach is evaluated using a synthetic dataset generated by simulating the social behaviour of the spammers and the non-spammers. The evaluation results reveal that the proposed approach is highly effective in blocking spammer with 2% false positive rate under a large number of spammers. Moreover, the proposed approach does not require any change in the underlying VoIP network architecture, and also does not introduce any additional signalling delay in a call set-up phase.
Author(s): Azad MA, Morla R, Arshad J, Salah K
Publication type: Article
Publication status: Published
Journal: Security and Communication Networks
Year: 2016
Volume: 9
Issue: 18
Pages: 4827–4838
Print publication date: 01/12/2016
Online publication date: 16/11/2016
Acceptance date: 06/08/2016
ISSN (print): 1939-0114
ISSN (electronic): 1939-0122
Publisher: John Wiley and Sons Inc.
URL: https://doi.org/10.1002/sec.1656
DOI: 10.1002/sec.1656
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