Deception Detection on the Internet

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Author(s): Xiaoling Chen (Stevens Institute of Technology, USA), Rohan D.W. Perera (Stevens Institute of Technology, USA), Ziqian (Cecilia) Dong (Stevens Institute of Technology, USA), Rajarathnam Chandramouli (Stevens Institute of Technology, USA)and Koduvayur P. Subbalakshmi (Stevens Institute of Technology, USA)
Copyright: 2010
Pages: 21
Source title: Handbook of Research on Computational Forensics, Digital Crime, and Investigation: Methods and Solutions
Source Author(s)/Editor(s): Chang-Tsun Li (University of Warwick, UK)
DOI: 10.4018/978-1-60566-836-9.ch014

Keywords: Digital Crime & Forensics / Information Science Reference / IT Security and Ethics / Security & Forensics

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Abstract

This chapter provides an overview of techniques and tools to detect deception on the Internet. A classification of state-of-the-art hypothesis testing and data mining based deception detection methods are presented. A psycho-linguistics based statistical model for deception detection is also described in detail. Passive and active methods for detecting deception at the application and network layer are discussed. Analysis of the pros and cons of the existing methods is presented. Finally, the inter-play between psychology, linguistics, statistical modeling, network layer information and Internet forensics is discussed along with open research challenges.

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