Neminath Hubballi, Santosh Biswas and Sukumar Nandi Towards reducing false alarms in network intrusion detection systems with data summarization technique Security and Communication Networks 6
State-of-the-art clustering-based anomaly detection systems require more than one pass on the training dataset to build normal system behavior. As networks become faster in operation, the amount of data that need to be processed is increasing and these clustering algorithms become expensive to work with. In this article, we use a data summarization-based algorithm to handle vast training data. Experimental results on three different datasets illustrate the efficacy of this method for handling large data and increasing accuracy.
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