SPIT callers detection with unsupervised Random Forests classifier

Kentaroh Toyoda, Iwao Sasase

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

As VoIP (Voice over IP) grows rapidly, it is expected to prevail tremendous unsolicited advertisement calls, which type of calls is referred to SPIT (SPam over Internet Telephony). SPIT detection is more difficult to execute than email SPAM detection since the callee or SPIT detection system does not tell whether it is SPIT or legitimate call until he/she actually takes a call. Recently, many SPIT detection techniques are proposed by finding outliers of call patterns. However, most of these techniques suffer from setting a threshold to distinguish that the caller is legitimate or not and this could cause to high false negative rate or low true positive rate. This is because these techniques analyse call pattern by a single feature e.g. call frequency or average call duration. In this paper, we propose a multi-feature call pattern analysis with unsupervised Random Forests classifier, which is one of the excellent classification algorithms. We also propose two simple but helpful features for better classification. We show the effectiveness of Random Forests based classification without supervised training data and which features contribute to classification.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Communications, ICC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2068-2072
Number of pages5
ISBN (Print)9781467331227
DOIs
Publication statusPublished - 2013 Jan 1
Event2013 IEEE International Conference on Communications, ICC 2013 - Budapest, Hungary
Duration: 2013 Jun 92013 Jun 13

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607

Other

Other2013 IEEE International Conference on Communications, ICC 2013
CountryHungary
CityBudapest
Period13/6/913/6/13

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'SPIT callers detection with unsupervised Random Forests classifier'. Together they form a unique fingerprint.

  • Cite this

    Toyoda, K., & Sasase, I. (2013). SPIT callers detection with unsupervised Random Forests classifier. In 2013 IEEE International Conference on Communications, ICC 2013 (pp. 2068-2072). [6654830] (IEEE International Conference on Communications). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC.2013.6654830