Citation

Hadjidj R, Debbabi M, Lounis H, Iqbal F, Szporer A, Benredjem D. Digital investigation 2009; 5(3-4): 124-137.

Copyright

(Copyright © 2009, Elsevier Publishing)

DOI

10.1016/j.diin.2009.01.004

PMID

unavailable

Abstract

Due to its simple and inherently vulnerable nature, e-mail communication is abused for numerous illegitimate purposes. E-mail spamming, phishing, drug trafficking, cyber bullying, racial vilification, child pornography, and sexual harassment are some common e-mail mediated cyber crimes. Presently, there is no adequate proactive mechanism for securing e-mail systems. In this context, forensic analysis plays a major role by examining suspected e-mail accounts to gather evidence to prosecute criminals in a court of law. To accomplish this task, a forensic investigator needs efficient automated tools and techniques to perform a multi-staged analysis of e-mail ensembles with a high degree of accuracy, and in a timely fashion. In this article, we present our e-mail forensic analysis software tool, developed by integrating existing state-of-the-art statistical and machine-learning techniques complemented with social networking techniques. In this framework we incorporate our two proposed authorship attribution approaches; one is presented for the first time in this article. (C) 2009 Elsevier Ltd. All rights reserved.

Language: en