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In this paper, the authors propose a framework for real-time alert correlation which incorporates novel techniques for aggregating alerts into structured patterns and incremental mining of frequent structured patterns. Their approach to aggregation provides a reduced view of developed patterns of alerts. At the core of the proposed framework is a new algorithm (FSP Growth) for mining frequent patterns of alerts considering their structures. In the proposed framework, time-sensitive statistical relationships between alerts are maintained in an efficient data structure and are updated incrementally to reflect the latest trends of patterns.
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