Intelligent intrusion detection systems can only be built if there is availability of an effective data set. A data set with a sizable amount of quality data which mimics the real time can only help to train and test an intrusion detection system. The NSL-KDD data set is a refined version of its predecessor KDD’99 data set. In this paper, the NSL-KDD data set is analyzed and used to study the effectiveness of the various classification algorithms in detecting the anomalies in the network traffic patterns.
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