Mitigating SPIT with Social Strength

Abstract

SPIT (Spam over Internet Telephony) is unsolicited, unwanted phone calls made for advertising products or voice phishing. The real time nature of voice calls makes traditional email anti-spam techniques un-applicable to SPIT detection in a VoIP (Voice over Internet Protocol) network. The VoIP users have social network with colleagues, friends, family members, and other acquaintances. Various social reputation approaches have been proposed but these were mainly based on average call duration or require user feedback to assign reputation score. We believe that the computation of reputation should be two fold; firstly it should not involve user feedback and secondly it considers other network features in addition to call duration. In this paper, we propose a social strength for detecting SPIT callers. We analyze how similarities and social ties among VoIP users effect SPIT detection. The local social strength among users are computed considering more features like out-degree, number of repetitive calls, reciprocity and interaction rate. The global strength of the caller is computed using the Eigen trust algorithm and represents the strength of a caller as whole in a network. The global strength values are then compared with the automated threshold value for finally classifying a caller as legitimate and non-legitimate. A distinct feature of our approach is that it does not involve users for feedback and can be easily deployed in real VoIP network without any change in architecture and SIP protocol stack. We evaluate our social strength approach on different types of random network data and shows that the system detects SPIT callers with false positive rate less then 10% and true positive rate of 99% for all type of underlying random networks.