Machine Learning and Deep Learning approaches in Network Intrusion Detection
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loginABSTRACT
This paper presents multiple hybrid models for Intrusion detection systems (IDS). Some of the proposed models use combination of information-gain based feature selection followed by classification using Random Forests and Naïve Bayes algorithms. Some of the proposed model use combination of expectation-maximization based clustering, information-gain based feature selection and then feed forward neural network with the backpropagation training algorithm. NSL-KDD dataset has been used to train and validate the model and NSL[1]KDD Test dataset is used to test the accuracy, precision, recall and F1-score of each of the proposed model. Performance of the proposed models is also compared with performance of Random Forests and Naïve Bayes based classification. The experimental results on the model that uses combination of expectation-maximization based clustering, information[1]gain based feature selection and then feed forward neural network showed promising results on detecting the intrusion when tested on NSL-KDD Test dataset.
Keywords – Network Intrusion Detection Systems, Deep Learning, Artificial Intelligence, NSL-KDD, NIDS, Artificial Intelligence in Network Security
