Language and gender author cohort analysis of e-mail for computer forensics

Proceedings of the Digital Forensic Research Conference, DFRWS 2002 USA

Volumn , Issue , 2002, Pages 2-16

  de Vel, Olivier ^a   Corney, Malcolm ^b   Anderson, Alison ^b   Mohay, George ^b  

^a DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION   (Australia)

^b QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER CRIME; ELECTRONIC CRIME COUNTERMEASURES; ELECTRONIC MAIL; LEARNING ALGORITHMS; MACHINE LEARNING; SUPPORT VECTOR MACHINES;

COHORT ANALYSIS; E-MAIL DOCUMENTS; LANGUAGE FEATURES; STRUCTURAL CHARACTERISTICS; STYLE MARKERS; TEXT DOCUMENT;

COMPUTER FORENSICS;

EID: 85057953322     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Apte, C., Damerau, F., Weiss, S.: Text mining with decision rules and decision trees. Workshop on Learning from text and the Web, Conference on Automated Learning and Discovery (1998)
2. Yang, Y., Liu, X.: A re-examination of text categorisation methods. Proc. 22nd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR99) (1999) 67–73
3. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. Proc. European Conf. Machine Learning (ECML’98) (1998) 137–142
4. Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A Bayesian approach to filtering junk e-mail. Learning for Text Categorization Workshop: 15th National Conf. on AI. AAAI Technical Report (WS-98-05) (1998) 55–62
5. Mosteller, F., Wallace, D.: Inference and Disputed Authorship: The Federalist. Addison-Wesley, Reading, Mass. (1964)
6. Elliot, E., Valenza, R.: Was the Earl of Oxford the true Shakespeare? Notes and Queries, 38 (1991) 501–506
7. Tweedie, F., Baayen, R.: How variable may a constant be? Measure of lexical richness in perspective. Computers and the Humanities, 32 (1998) 323–352
8. Farringdon, J.: Analysing for Authorship: A Guide to the Cusum Technique. University of Wales Press, Cardi (1996)
9. Tweedie, F., Singh, S., Holmes, D.: Neural network applications in stylometry: The Federalist papers. Computers and the Humanities (1996) 1–10
10. Corney, M., Anderson, A., Mohay, G., de Vel, O.: Identifying the Authors of Suspect E-mail. Computers and Security (submitted) (2001)
11. de Vel, O., Anderson, A., Corney, M., Mohay, G.: E-mail Authorship Attribution for Computer Forensics. Data Mining for Security Applications, Kluwer Publishers (2002)
12. Thomson, R., Murachver, T.: Predicting gender from electronic discourse. British Journal of Social Psychology 40 (2001) 193–208
13. Schiman, H.: Bibliography of gender and language. http://ccat.sas.upenn.edu/ haroldfs/popcult/bibliogs/gender/genbib.htm (2002)
14. Vapnik, V.: The Nature of Statistical Learning Theory. Springer-Verlag, New York (1995)
15. Druker, H., Wu, D., Vapnik, V.: Support vector machines for spam categorisation. IEEE Trans. on Neural Networks, 10 (1999) 1048–1054
16. Diederich, J. Kindermann, J., Leopold, E., Paass, G.: Authorship attribution with Support Vector Machines. Applied Intelligence (2000) (submitted)
17. de Vel, O.: Mining e-mail authorship. Proc. Workshop on Text Mining, ACM International Conference on Knowledge Discovery and Data Mining (KDD’2000) (2000)
18. Chaski, C.: A Daubert-inspired assessment of current techniques for language-based author identification. US National Institute of Justice (1998) http://www.ncjrs.org
19. Support Vector Machine, SVMLight: University of Dortmund (2001) http://wwwai.cs.uni-dortmund.de/FORSCHUNG/VERFAHREN/SVM LIGHT/svm light.eng.html
20. Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
21. Chaski, C.: Empirical evaluations of language-based author identification techniques. Forensic Linguistics, 8 (2001)