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This paper introduces the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining techniques to automatically detect attacks against computer networks and systems. While the long-term objective of MINDS is to address all aspects of intrusion detection, this paper focuses on two specific contributions. First, it shows how the behavior-based anomaly detection approach of MINDS is suitable for detecting new and previously unknown types of intrusions, which often indicate emerging threats. Second, it shows how association pattern analysis can be used to summarize and characterize anomalous network connections.
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