@inproceedings{76e3c39771a143bda9ac21b32d3aecaf,
title = "Adaptive context modeling for deception detection in emails",
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%).",
keywords = "Deception Detection, Entropy, Prediction by Partial Matching, Suffix Tree",
author = "Peng Hao and Xiaoling Chen and Na Cheng and R. Chandramouli and Subbalakshmi, {K. P.}",
year = "2011",
doi = "10.1007/978-3-642-23199-5_34",
language = "English",
isbn = "9783642231988",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "458--468",
booktitle = "Machine Learning and Data Mining in Pattern Recognition - 7th International Conference, MLDM 2011, Proceedings",
note = "7th International Conference on Machine Learning and Data Mining, MLDM 2011 ; Conference date: 30-08-2011 Through 03-09-2011",
}