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Abstract: In this paper we describe a data mining framework for constructing intrusion detection models. The key ideas are to mine system audit data for consistent and useful patterns of program and user behavior, and use the set of relevant system features presented in the patterns to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Our experiments on audit data of system programs and network activities showed that classification models can detect intrusions,... (Update)
Context of citations to this paper: More
...al. 1997) is a meta learning base distributed data mining framework has been used for fraud detection in the banking domain (Lee, Stolfo, Mok, 1999). Collective data mining (Kargupta, Johnson, Riva Sanseverino, Park, Silvestre, Hershberger, 1998; Kargupta, Park,...
.... system (Stolfo et al. 1997) is a distributed data mining framework used primarily for fraud detection in the banking domain (Lee, Stolfo, Mok, 1999). This system develops patterns of fraudulent activity by mining the individual databases of the various financial...
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BibTeX entry: (Update)
Lee, W., Stolfo, S., & Mok, K. (1999). A data mining framework for adaptive intrusion detection. To appear in the Proceedings of the 1999 IEEE Symposium on Security and Privacy, IEEE Computer Society Press. http://citeseer.ist.psu.edu/article/lee98data.html More
@inproceedings{ lee98data,
author = "Wenke Lee and Salvatore Stolfo",
title = "Data mining approaches for intrusion detection",
booktitle = "Proceedings of the 7th {USENIX} Security Symposium",
address = "San Antonio, TX",
year = "1998",
url = "citeseer.ist.psu.edu/article/lee98data.html" }
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