Adaptive context modeling for deception detection in emails

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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%).

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 7th International Conference, MLDM 2011, Proceedings
Pages458-468
Number of pages11
DOIs
StatePublished - 2011
Event7th International Conference on Machine Learning and Data Mining, MLDM 2011 - New York, NY, United States
Duration: 30 Aug 20113 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6871 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Machine Learning and Data Mining, MLDM 2011
Country/TerritoryUnited States
CityNew York, NY
Period30/08/113/09/11

Keywords

  • Deception Detection
  • Entropy
  • Prediction by Partial Matching
  • Suffix Tree

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