A Network Intrusion Detection System Based on Categorical Boosting Technique using NSL-KDD

Abstract

Massive volumes of network traffic & data are generated by common technology including the Internet of Things, cloud computing & social networking. Intrusion Detection Systems are therefore required to track the network which dynamically analyses incoming traffic. The purpose of the IDS is to carry out attacks inspection or provide security management with desirable help along with intrusion data. To date, several approaches to intrusion detection have been suggested to anticipate network malicious traffic. The NSL-KDD dataset is being applied in the paper to test intrusion detection machine learning algorithms. We research the potential viability of ELM by evaluating the advantages and disadvantages of ELM. In the preceding part on this issue, we noted that ELM does not degrade the generalisation potential in the expectation sense by selecting the activation function correctly. In this paper, we initiate a separate analysis & demonstrate that the randomness of ELM often contributes to some negative effects. For this reason, we have employed a new technique of machine learning for overcoming the problems of ELM by using the Categorical Boosting technique (CATBoost).