Contemporary Machine Learning Approach for Anomaly Based Network Intrusion Detection System

Networks must be protected from suspicious activity using anomaly-based network intrusion detection. In the past, machine learning techniques have become more important while addressing network security issues. High detection rates and low false alarm rates are the main objectives of intrusion detection systems (IDS). Despite being widely used, classification-based machine learning methods are ineffective in identifying unidentified threats in the network. In our research, machine learning techniques are primarily used to create anomaly-based intrusion detection systems that draw information from the training phase from the data. However, current intrusion detection techniques cover the most recent kinds of behaviors like DDoS attackers, Probes, U2Rs, and R2Ls, the difficult problem in this section is low false alarm rate. Nearly all network IDS rely on the network's deployed environment. Our suggested approach offers a quick and precise features selection process while attempting to be independent of the deployed environment network.