Title :
E-mail authorship mining based on SVM for computer forensic
Author :
Teng, Gui-fa ; Lai, Mao-sheng ; Ma, Jian-Bin ; Li, Ying
Author_Institution :
Dept. of Inf. Manage., Peking Univ., Beijing, China
Abstract :
We describe our work which attempts to mine e-mail authorship for the purpose of computer forensic. We extract various e-mail document features including linguistic features, header features and structural characteristics. These features together are used with the support vector machine learning algorithm to classify or attribute authorship of e-mail messages to an author. The primary experiments on a number of e-mail documents have given ideal results, which indicate that the project has laid a firm groundwork for the future work.
Keywords :
computer crime; data mining; electronic mail; feature extraction; legislation; support vector machines; SVM; computer forensic; e-mail authorship mining; e-mail messages; feature extraction; linguistic features; support vector machine learning algorithm; Data mining; Electronic mail; Forensics; Information management; Machine learning; Support vector machine classification; Support vector machines; Text categorization; Unsolicited electronic mail; Writing;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1382374