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Abstract: We are developing a prototype intelligent intrusion detection system (IIDS) to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic and genetic algorithms. This system combines both anomaly based intrusion detection using fuzzy data mining techniques and misuse detection using traditional rule-based expert system techniques. The anomaly-based components are developed using fuzzy data mining techniques. They look for deviations from stored patterns of normal ... (Update)
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BibTeX entry: (Update)
@misc{ bridges-fuzzy,
author = "Susan M. Bridges",
title = "Fuzzy Data Mining And Genetic Algorithms Applied To Intrusion Detection",
url = "citeseer.ist.psu.edu/587469.html" }
Citations (may not include all citations):910 Fast algorithms for mining association rules - Agrawal - 1999
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105 State transition analysis: A rule-based intrusion detection .. - Ilgun - 1995
85 Discovering generalized episodes using minimal occurrences - Mannila - 1999
35 Mining audit data to build intrusion detection models - Lee, Stolfo et al. - 1998
35 State of the Practice of Intrusion Detection Technologies - Allen - 2000
34 A neural network component for an intrusion detection system (context) - Debar, Becker et al. - 1992
28 Adaptive real-time anomaly detection using inductively gener.. (context) - Teng, Chen et al. - 1990
23 Detecting intruders in computer systems - Lunt - 1999
12 Mining fuzzy association rules in databases - Kuok, Fu et al. - 2000
7 Live traffic analysis of TCP/IP gateways (context) - Porras - 1999
4 FuzzyCLIPS version 6 (context) - Orchard - 1995
4 Integrating fuzzy logic with data mining methods for intrusi.. - Luo - 1999
3 Integrating data mining techniques with intrusion detection .. (context) - Mukkamala, Gagnon et al. - 2000
1 Genetic algorithm optimization of membership functions for m.. (context) - Wang - 2000
1 The Application of Genetic Algorithms for Feature Selection .. (context) - Fajun, Bridges et al. - 2000
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