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The Fuzzy Intrusion Recognition Engine (FIRE) is an anomaly-based intrusion detection system that uses fuzzy logic to assess whether malicious activity is taking place on a network. It uses simple data mining techniques to process the network input data and help expose metrics that are particularly significant to anomaly detection. These metrics are then evaluated as fuzzy sets. FIRE uses a fuzzy analysis engine to evaluate the fuzzy inputs and trigger alert levels for the security administrator. This paper describes the components in the FIRE architecture and explains their roles. Particular attention is given to explaining the benefits of data mining and how this can improve the meaningfulness of the fuzzy sets. Fuzzy rules are developed for some common intrusion detection scenarios. The results of tests with actual network data and actual malicious attacks are described. The FIRE IDS can detect a wide-range of common attack types.
Dickerson et al. (Fri,) studied this question.
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