SPIT callers detection with unsupervised Random Forests classifier

Kentaroh Toyoda, Iwao Sasase

研究成果: Conference contribution

14 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2013 IEEE International Conference on Communications, ICC 2013
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2068-2072
ページ数5
ISBN(印刷版)9781467331227
DOI
出版ステータスPublished - 2013 1月 1
イベント2013 IEEE International Conference on Communications, ICC 2013 - Budapest, Hungary
継続期間: 2013 6月 92013 6月 13

出版物シリーズ

名前IEEE International Conference on Communications
ISSN(印刷版)1550-3607

Other

Other2013 IEEE International Conference on Communications, ICC 2013
国/地域Hungary
CityBudapest
Period13/6/913/6/13

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学

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