Intrusion detection in web applications using text mining

Engineering Applications of Artificial Intelligence

Volumn 20, Issue 4, 2007, Pages 555-566

  García Adeva, Juan José ^a   Pikatza Atxa, Juan Manuel ^b  

^a UNIVERSITY OF SYDNEY   (Australia)

^b UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Document classification; Machine learning; Text categorisation; Text mining; Web intrusion detection; Web mining

Indexed keywords

ACCESS CONTROL; COMPUTER SOFTWARE; CRYPTOGRAPHY; DATABASE SYSTEMS; INTRUSION DETECTION; LEARNING SYSTEMS; TELECOMMUNICATION TRAFFIC; TELEMEDICINE; WEB SERVICES;

CODE WRITING; DOCUMENT CLASSIFICATION; LOG ENTRIES; SYSTEM MAINTAINABILITY; TEXT CATEGORISATION; UNAUTHORISED ACCESS;

DATA MINING;

EID: 33947190435     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2006.09.001     Document Type: Article

References (23)

1. Amor N.B., Benferhat S., and Elouedi Z. Naive Bayes vs decision trees in intrusion detection systems. SAC '04: Proceedings of the 2004 ACM Symposium on Applied Computing (2004), ACM Press, New York, NY, USA 420-424
2. Barbara, D., Couto, J., Jajodia, S., Wu, N., 2001. Adam: a testbed for exploring the use of data mining in intrusion detection. In: Special Section on Data Mining for Intrusion Detection and Threat Analysis, vol. 30(4). ACM Press, New York, NY, USA, pp. 15-24.
3. Chan, P.K., Mahoney, M.V., Arshad, M.H., 2003. A machine learning approach to anomaly detection. Technical Report, Florida Institute of Technology.
4. Denning D.E. An intrusion-detection model. IEEE Transactions on Software Engineering 13 2 (1987) 222-232
5. EU Parliament, 1997. Directives 97/46/CE and 97/66/CE.
6. Fawcett, T., 2003. ROC graphs: notes and practical considerations for researchers. Technical Report, HP Laboratories.
7. García Adeva, J.J., 2006. Serving text-mining functionalities with the software architecture Plato. Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation. IEEE Computer Society, in press.
8. García Adeva J.J., and Calvo R. Mining text with Pimiento. IEEE Internet Computing 10 4 (2006) 27-35
9. Hearst, M.A., 1999. Untangling text data mining. In: Proceedings of the 37th Conference on Association for Computational Linguistics. Association for Computational Linguistics, College Park, Maryland, pp. 3-10.
10. Howard, J.D., 1998. An analysis of security incidents on the internet 1989-1995. Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA, USA.
11. Kumar, S., Spafford, E., 1994. An application of pattern matching in intrusion detection. Technical Report 94-013, Department of Computer Sciences, Purdue University.
12. Lane T., and Brodley C.E. An application of machine learning to anomaly detection. Proceedings of the 20th NIST-NCSC National Information Systems Security Conference (1997) 366-380
13. Lee, W., Stolfo, S., 1998. Data mining approaches for intrusion detection. In: Proceedings of the Seventh USENIX Security Symposium, San Antonio, TX.
14. Lewis D.D. Naive (Bayes) at forty: the independence assumption in information retrieval. In: Nédellec C., and Rouveirol C. (Eds). Proceedings of ECML-98, 10th European Conference on Machine Learning (1998), Springer, Heidelberg, DE, Chemnitz, DE 4-15
15. Mansfield G., Ohta K., Takei Y., Kato N., and Nemoto Y. Towards trapping wily intruders in the large. Computer Networks 34 4 (2000) 659-670
16. McCallum, A., Nigam, K., 1998. A comparison of event models for Naive Bayes text classification. In: Proceedings of AAAI-98 Workshop on Learning for Text Categorization. Madison, Wisconsin, pp. 137-142.
17. Min. de Administraciones Públicas, 1999. Ley orgánica 15/1999, de 13 de diciembre, de protección de datos de carácter personal.
18. Moschitti A. A study on optimal parameter tuning for Rocchio Text Classifier. Proceedings of the 25th European Conference on Information Retrieval Research (2003)
19. Pikatza, J.M., Larburu, I.U., Sobrado, F.J., García Adeva, J.J., 2002. Arnasa: una forma de desarrollo basado en el dominio en la construcción de un DSS para la gestión del proceso de tratamiento del asma vía Web. In: Seventh Software Engineering and Data Bases Conference (JISBD), Madrid, Spain.
20. Provost F.J., and Fawcett T. Robust classification for imprecise environments. Machine Learning 42 3 (2001) 203-231
21. Sobrado F.J., Pikatza J.M., Larburu I.U., García Adeva J.J., and López de Ipiña D. Towards a clinical practice guideline implementation for asthma treatment. Lecture Notes in Artificial Intelligence 3040 (2004) 587-596
22. Yang Y., and Chute C.G. An example-based mapping method for text categorization and retrieval. ACM Transactions on Information Systems 12 3 (1994) 252-277
23. Yang, Y., Liu, X., 1999. A re-examination of text categorization methods. In: 22nd Annual International SIGIR. Berkeley, pp. 42-49.