Intrusion Detection via Fuzzy Data Mining
نویسندگان
چکیده
This paper describes a prototype intelligent intrusion detection system (IIDS) that is being developed to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic. This system combines two distinct intrusion detection approaches: 1) anomaly based intrusion detection using fuzzy data mining techniques, and 2) misuse detection using traditional rule-based expert system techniques. The anomaly-based components look for deviations from stored patterns of normal behavior. The misuse detection components look for previously described patterns of behavior that are likely to indicate an intrusion. Both network traffic and system audit data are used as inputs. Accepted for Presentation at The Twelfth Annual Canadian Information Technology Security Symposium June 19-23, 2000, The Ottawa Congress Centre INTRUSION DETECTION VIA FUZZY DATA MINING
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