Novel Unsupervised SPITters Detection Scheme by Automatically Solving Unbalanced Situation

Abstract

Spam over Internet telephony (SPIT) is recognized as a new threat for voice communication services such as voice over Internet protocol (VoIP). Due to the privacy reason, it is desired to detect SPITters (SPIT callers) in a VoIP service without training data. Although a clustering-based unsupervised SPITters detection scheme has been proposed, it does not work well when the SPITters account for a small fraction of the entire caller. In this paper, we propose an unsupervised SPITters detection scheme by adding artificial SPITters data to solve the unbalanced situation. The key contribution is to propose a novel way to automatically decide how much artificial data should be added. We show that classification performance is improved by means of computer simulation with real and artificial call log data sets.

Keywords

• SPIT (spam over internet telephony)
• unsupervised learning
• VoIP (voice over internet protocol)
• security