Abstract
Nowadays, IT systems are everywhere like in an organization, enterprise, and institution. Among organizational assets, IT assets are the hardest to nail down as it requires many repetitive technical works to identify intangible things like data, software, and configurations. To protect dangerous impacts on the IT infrastructure, this research focuses on the development of a network intrusion detection model that performs accurate detection at a low false-positive rate. We consider the significant role of the standard protocols used to launch an attack, and therefore developed the detection model based on the protocol datasets. We utilized the research methods of the random forest as the classifier and genetic algorithm for feature selection. Three protocol datasets, which represent subsets of the NSL-KDD dataset, are used for the experiment. The finding is sufficient to prove that our protocol random forest (Protocol-RF) model detects the attacks with the highest precision of 0.907 and a low false-positive rate of 0.001.
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Htwe, T.T., Kham, N.S.M. (2021). Protocol Random Forest Model to Enhance the Effectiveness of Intrusion Detection Identification. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-15-5148-2_15
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DOI: https://doi.org/10.1007/978-981-15-5148-2_15
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