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Current system managers often have to process huge amounts of alerts per day, which may be produced by all kinds of security products, network management tools or system logs. This has made it extremely difficult for managers to analyze and react to threats and attacks. So an effective technique which can automatically filter and analyze alerts has become urgent need. This paper presents a novel method for handling IDS alerts more efficiently. It introduces a new data mining technique, outlier detection, into this field, and designs a special outlier detection algorithm for identifying true alerts and reducing false positives (i.e. alerts that are triggered incorrectly by benign events).
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