Identifying the Authors of Suspect Email
In this paper, we present the results of an investigation into identifying the authorship of email messages by analysis of the contents and style of the email messages themselves. A set of stylistics features applicable to text in general and an extended set of email-specific structural features were identified. A Support Vector Machine learning method was used to discriminate between the authorship classes. Through a series of baseline experiments on non-email data, it was found that approximately 20 email messages with approximately 100 words in each message should be sufficient to discriminate authorship in most cases. These results were confirmed with a corpus of email data and performance was further enhanced when a set of email-specific features were added. This outcome has important implications in the management of such problems as email abuse, anonymous email messages and computer forensics.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Journal Article|
|Keywords:||authorship attribution, email, stylometry, machine learning, support vector machine, data mining|
|Subjects:||Australian and New Zealand Standard Research Classification > LANGUAGES COMMUNICATION AND CULTURE (200000) > LINGUISTICS (200400) > Computational Linguistics (200402)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Natural Language Processing (080107)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Institutes > Information Security Institute
|Copyright Owner:||Copyright 2001 QUT and (The authors)|
|Deposited On:||06 Jun 2007|
|Last Modified:||23 Dec 2014 22:13|
Repository Staff Only: item control page