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Abstract: We describe our research in applying data mining techniques to construct 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 past experiments showed that classification rules can be used to detect intrusions, provided that sufficient audit data ... (Update)
Context of citations to this paper: More
...attributes and the goal attribute. Some researches on integrating classification and association rule mining are described in [6] [7] and [8] In [6] the authors have proposed a new general model called CBA (Classification Based on Association) that consists of two parts,...
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BibTeX entry: (Update)
Lee, W., Stolfo, S., and Mok, K. (1999d). Algorithms for Mining System Audit Data. In Lin, T. Y. and Cercone, N., editors, Data Retrieval and Data Mining. Kluwer Academic Publishers. http://citeseer.ist.psu.edu/lee99algorithms.html More
@incollection{ lee-algorithms,
author = "W. Lee and S. Stolfo and K. Mok",
title = "Algorithms for Mining System Audit Data",
editor = "Lin, T. Y. and Cercone, N.",
booktitle = "Data Retrieval and Data Mining",
publisher = "Kluwer Academic Publishers",
url = "citeseer.ist.psu.edu/lee99algorithms.html" }
Citations (may not include all citations):921 Mining association rules between sets of items in large data.. - Agrawal, Imielinski et al. - 1993 ACM DBLP
910 Fast algorithms for mining association rules - Agrawal, Srikant - 1994 ACM
340 Mining sequential patterns - Agrawal, Srikant - 1995 ACM DBLP
213 Discovery of multiple-level association rules from large dat.. - Han, Fu - 1995 ACM DBLP
189 Discovering frequent episodes in sequences (context) - Mannila, Toivonen et al. - 1995
137 Finding interesting rules from large sets of discovered asso.. - Klemettinen, Mannila et al. - 1994
121 Mining association rules with item constraints - Srikant, Vu et al. - 1997
105 State transition analysis: A rule-based intrusion detection .. - Ilgun, Kemmerer et al. - 1995 DBLP
87 ective rule induction (context) - Cohen - 1995
86 JAM: Java agents for meta-learning over distributed database.. - Stolfo, Prodromidis et al. - 1997
85 Discovering generalized episodes using minimal occurrences - Mannila, Toivonen - 1996
84 Data mining approaches for intrusion detection - Lee, Stolfo - 1998
78 Security problems in the tcp/ip protocol suite - Bellovin - 1989 ACM
59 Toward parallel and distributed learning by meta-learning - Chan, Stolfo - 1993
58 available via anonymous ftp to ftp (context) - Jacobson, Leres et al. - 1989
56 A real-time intrusion detection expert system (context) - Lunt, Tamaru et al. - 1992
56 Clustering association rules - Lent, Swami et al. - 1997
36 A belief-driven method for discovering unexpected patterns - Padmanabhan, Tuzhilin - 1998 DBLP
8 Adaptive intrusion detection: a data mining approach - Lee, Stolfo et al. - 1999
4 Test Center Comparison: Network intrusion-detection solution.. (context) - McClure, Scambray et al. - 1998
4 Mining in a data-flow environment: Experience in intrusion d.. (context) - Lee, Stolfo et al. - 1999
4 Unix system security (context) - Grampp, Morris - 1984 ACM
3 Decision tree induction based on e#cient tree restructuring (context) - Utgo, Berkman et al. - 1997
2 Combining data mining and machine learning for e#ective user.. (context) - Fawcett, Provost - 1996
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