Event Sequence Mining to Develop Profiles for Computer Forensic Investigation Purposes

Conferences in Research and Practice in Information Technology Series

Volumn 54, Issue , 2006, Pages 145-153

  Abraham, Tamas ^a  

^a DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION   (Australia)

Author keywords

Computer forensics; Data mining; Event sequences

Indexed keywords

EVENT SEQUENCE; FORENSIC INVESTIGATION; METHODOLOGY AND TECHNIQUES; USAGE PATTERNS;

BEHAVIORAL RESEARCH; DATA MINING; GRAPHICAL USER INTERFACES; GRID COMPUTING; NETWORK SECURITY;

COMPUTER FORENSICS;

EID: 84872855806     PISSN: 14451336     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (30)

1. Abbott, J. (2004), Writing log parsers for ECF, Research Report, Queensland University of Technology, 2004.
2. Abraham, T. & de Vel, O. (2002), Investigative profiling with computer forensic data and association rules, In Proceedings of the IEEE International Conference on Data Mining, Maebashi City, Japan.
3. Abraham, T., Kling, R. & de Vel, O. (2002), Investigative profile analysis with computer forensic log data using attribute generalisation, In Proceedings of the Australasian Data Mining Workshop, Canberra, Australia.
4. Adomavicius, G. & Tuzhilin, A. (2001), Expertdriven validation of rule-based user models in personalization applications, Data Mining and Knowledge Discovery, 5(1/2):33-58.
5. Adomavicius, G. & Tuzhilin, A. (2001), Using data mining methods to build customer profiles, Computer, 34(2):74-82.
6. Aggarwal, C., Sun, Z. & Yu, P. (1998), Online algorithms for finding profile association rules, In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM-98), Bethesda, MD, USA.
7. Agrawal, R., Imielinski, T. & Swami, A. (1993), Mining Associations between Sets of Items in Massive Databases, In Proc. of the ACM SIGMOD Int'l Conference on Management of Data, Washington D.C., pages 207-216.
8. Agrawal, R. & Srikant, R. (1995), Mining sequential patterns, In Proceedings of the Eleventh International Conference on Data Engineering, pages 3-14.
9. Casey, E. (2000), Digital Evidence and Computer Crime, Academic Press.
10. Chan, P. K. (1999), A non-invasive learning approach to building web user profiles, In B. Masand & M. Spiliopoulou, editors, Proceedings of the Workshop on Web Usage Analysis and User Profiling (WEBKDD'99).
11. de Vel, O., Anderson, A., Corney, M. & Mohay, G. (2001), Mining e-mail content for author identification forensics, SIGMOD Record, 30(4).
12. Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996), A density-based algorithm for discovering clusters in large spatial databases with noise, In Proceedings of the Second Int. Conference on Knowledge Discovery and Data Mining.
13. T. Fawcett & Provost, F. (1997), Adaptive fraud detection, Data Mining and Knowledge Discovery, 1(3):291-316.
14. Han, J., Cai, Y. & Cercone, N. (1992), Knowledge discovery in databases: an attribute-oriented approach, In Proceedings of 18th Int. Conference on Very Large Databases.
15. Han, J. & Fu, Y. (1995), Discovery of multiple-level association rules from large databases, In Proceedings of 21st VLDB Conference.
16. Hilderman R. J. & Hamilton, H. J. (1999), Heuristic measures of interestingness, In Proceedings of the 3rd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'99).
17. Hilderman R. J. & Hamilton, H. J. (1999), Knowledge discovery and interestingness measures: A survey, Technical Report CS-99-04, Dept of Computer Science, University of Regina.
18. Hirsh, M., Basu, C. & Davidson, B. (2000), Learning to personalize, Communications of the ACM, 43(8):102-106.
19. Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L. & Riedl, J. (1997), Grouplens: Applying collaborative filtering to usenet news, Communications of the ACM, 40(3):77-87.
20. Lu, H., Feng, L. & Han, J. (2000), Beyond intra-transaction association analysis: mining multi-dimensional inter-transaction rules, ACM Transactions on Information Systems, 18(4):423-454.
21. Mannila, H., Toivonen, H. & Verkamo, A. (1997), Discovery of Frequent Episodes in Event Sequences, Data Mining and Knowledge Discovery, 1(3):259-289.
22. Mobasher, B., Dai, H., Luo, T., Sun, Y. & Wiltshire, J. (2000), Discovery of aggregate usage profiles for web personalization, In Proceedings of the Workshop on Web Mining for E-Commerce (WEBKDD'00).
23. Nanopoulos, A., Katsaros, D. & Manolopoulos, Y. (2001), Effective prediction of web-user accesses: a data mining approach, In Proceedings of the Workshop on Mining Logdata Accross All Customer Touchpoints (WEBKDD'01).
24. Nesbitt, S. & de Vel, O. (1998), A collaborative filtering agent system for dynamic virtual communities on the web, In Proceedings of the Conference on Learning and Discovery (CONALD98).
25. Oates, T. & Cohen, P. R. (1996), Searching for structure in multiple streams of data, In Proceedings of the Thirteenth International Conference on Machine Learning.
26. Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U. & Hsu, M. (2004), Mining sequential patterns by pattern-growth: The PrefixSpan approach, In IEEE Trans. Knowledge and Data Engineering, 16(10):1-17.
27. Srikant, R. & Agrawal, R. (1995), Mining generalized association rules, In Proceedings of 21st VLDB Conference.
28. Stanfill, C. & Waltz, D. (1986), Toward memorybased reasoning, Communications of the ACM, 29(12):1213-1228.
29. Stevens, M. (2004), Unification of relative timeframes for digital forensics, Digital Investigation, 1(3):225-239.
30. Tan, P. N. & Kumar, V. (2001), Mining indirect associations in web data, In Proceedings of the Workshop on Mining Logdata Accross All Customer Touchpoints (WEBKDD'01).