Behavior-based approach to detect spam over IP telephony attacks

Spam over IP telephony (SPIT) is expected to become a serious problem as the use of voice over IP grows. This kind of spam is appreciated by spammers due to its effectiveness and low cost. Many anti-SPIT solutions are applied to resolve this problem but there are still limited in some cases. Thus, in this paper, we propose a system to detect SPIT attacks through behavior-based approach. Our framework operates in three steps: (1) collecting significant calls attributes by exploring and analyzing network traces using OPNET environment; (2) applying sliding windows strategy to properly maintain the callers profiles; and (3) classifying caller (i.e., legitimate or SPITter) using ten supervised learning methods: NaïveBayes, BayesNet, SMO RBFKernel, SMO PolyKernel, MultiLayerPerceptron with two and three layers, NBTree, J48, Bagging and AdaBoostM1. The results of our experiments demonstrate the great performance of these methods. Our study, based on receiver operating characteristics curves, shows that the AdaBoostM1 classifier is more efficient than the other methods and achieve an almost perfect detection rate with acceptable training time.