Adaptive context modeling for deception detection in emails

  • Authors:

  • Peng Hao;Xiaoling Chen;Na Cheng;R. Chandramouli;K. P. Subbalakshmi

  • Affiliations:

  • Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ

  • Venue:
  • MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
  • Year:
  • 2011

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Abstract

Deception detection in e-mails is addressed in this paper. An adaptive probabilistic context modeling method that spans information theory and suffix trees is proposed. Some properties of the proposed adaptive context model are also discussed. Experimental results on truthful (ham) and deceptive (scam) e-mail data sets are presented to evaluate the proposed detector. The results show that adaptive context modeling can result in high (93.33%) deception detection rate with low false alarm probability (2%).