(Enter summary)
Abstract: This paper describes a novel framework, MADAM ID, for Mining Audit Data for Automated Models for Intrusion Detection. This framework uses data mining algorithms to compute activity patterns from system audit data and extracts predictive features from the patterns. It then applies machine learning algorithms to the audit records that are processed according to the feature definitions to generate intrusion detection rules. Results from the 1998 DARPA Intrusion Detection Evaluation showed that our ... (Update)
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BibTeX entry: (Update)
Lee, W. and Stolfo, S. J., "A Framework for Constructing Features and Models for Intrusion Detection Systems," ACM Transactions on Information and System Security, vol. 3, November, 2000 http://citeseer.ist.psu.edu/article/lee00framework.html More
@article{ lee00framework,
author = "Wenke Lee and Salvatore J. Stolfo",
title = "A framework for constructing features and models for intrusion detection systems",
journal = "Information and System Security",
volume = "3",
number = "4",
pages = "227--261",
year = "2000",
url = "citeseer.ist.psu.edu/article/lee00framework.html" }
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