Abstract
The research in network intrusion detection has escalated since past few years. There are various methods and systems being proposed related to intrusion detection. But the changing nature of attack leads to the need to deal with every possible aspect to increase the detection efficiency. The anomaly-based intrusion detection has always been in the limelight because of its detection capability of the unknown pattern. In the study, feature reduction is performed using gain ratio, correlation, information gain and symmetrical uncertainty, and the selected features are used to train the machine learning techniques such as Naive Bayes classifier, sequential minimal optimization (SMO), J48 classifier and random forest. The KDD cup 99 and NSL-KDD datasets were used as a benchmark reference in building the models. The experiment was carried out with an aim to compare the performance of various classifiers between the two datasets. Results show that the feature reduction improves the detection efficiency and can obtain accuracy as high as 99.91%.
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Devi, T.J., Singh, K.J. (2021). Anomaly-Based Intrusion Detection System in Two Benchmark Datasets Using Various Learning Algorithms. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Computing Techniques and Applications. Smart Innovation, Systems and Technologies, vol 225. Springer, Singapore. https://doi.org/10.1007/978-981-16-0878-0_19
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DOI: https://doi.org/10.1007/978-981-16-0878-0_19
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