Accuracy Analysis of Machine Learning Algorithms for Intrusion Detection System Using NSL-KDD Dataset
Intrusion Detection System (IDS) that turns to be a vital component to secure the network. The lack of regular updation, less capability to detect unknown attacks, high non-adaptable false alarm rate, more consumption of network resources, etc., makes IDS to compromise. This paper aims to classify the NSL-KDD dataset with respect to their metric data by using the best six data mining classification algorithms like J48, ID3, CART, Bayes Net, Naïve Bayes and SVM to find which algorithm will be able to offer more testing accuracy. NSL-KDD dataset has solved some of the inherent limitations of the available KDD’99 dataset.
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