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An approach to combating network intrusion is the development of systems applying machine learning and data mining techniques. Many IDS (Intrusion Detection Systems) suffer from a high rate of false alarms and missed intrusions. It wanted to be able to improve the intrusion detection rate at a reduced false positive rate. The focus of this paper is rule-learning, using RIPPER, on highly imbalanced intrusion datasets with an objective to improve the true positive rate (intrusions) without significantly increasing the false positives.
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