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Abstract: With the continuous evolution of the types of attacks against computer networks, traditional intrusion detection systems, based on pattern matching and static signatures, are increasingly limited by their need of an up-to-date and comprehensive knowledge base. Data mining techniques have been successfully applied in host-based intrusion detection. Applying data mining techniques on raw network data, however, is made di#cult by the sheer size of the input; this is usually avoided by discarding... (Update)
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3: Computer Security Threat Monitoring and Surveillance (context) - Anderson - 1980
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BibTeX entry: (Update)
Zanero, S. & Savaresi, S. (2004), Unsupervised learning techniques for an intrusion detection system. http://citeseer.ist.psu.edu/zanero04unsupervised.html More
@inproceedings{ zanero-savaresi,
author = {Stefano Zanero and Sergio M. Savaresi},
title = {Unsupervised learning techniques for an intrusion
detection system},
booktitle = {Proc. of the 2004 ACM Symposium on Applied
Computing},
year = 2004,
pages = {412--419},
publisher = {ACM Press},
isbn = {1-58113-812-1},
location = {Nicosia, Cyprus},
url = {citeseer.ist.psu.edu/zanero04unsupervised.html} }
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