Abstract: A lot of information systems are protected by and have damage minimized by intrusion detection systems. It defends computer networks, both virtual and physical, from dangers and weaknesses. Machine learning methods are currently being widely expanded to create efficient intrusion detection systems. Machine learning techniques for intrusion detection include rule learning, ensemble approaches, statistical models, and neural networks. Machine learning ensemble approaches stand out among them for their effectiveness in the learning process. This study aims to increase detection rate accuracy for all attack kinds and individual attack types, which will aid in the identification of attacks and specific categories of attacks. K-fold cross validation is used to assess the suggested approach, and the experimental outcomes of all three classifiers are examined. UNSW-NB15 dataset is used to measure the performance of the proposed approach in order to guarantee its efficiency.

Keywords: Ensemble Learning, Network Intrusion Detection, , Multi-classification, Random Forest.

PDF | DOI: 10.17148/IJARCCE.2023.12719

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