Novel Unsupervised SPITters Detection Scheme by Automatically Solving Unbalanced Situation

Kentaroh Toyoda, Mirang Park, Naonobu Okazaki, Tomoaki Ohtsuki

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Article number7792553
Pages (from-to)6746-6756
Number of pages11
JournalIEEE Access
Volume5
DOIs
Publication statusPublished - 2017

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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