A Hybrid Machine Learning Based Feature Selection Technique for Attack Detection in NIDS

Abstract

Network security has arisen as a critical investigation area in the last period as the internet and communiqué technologies have grown in popularity. The network is protected by firewalls, antivirus software, and intrusion detection systems (IDS) and its related assets in a cyberspace against unauthorised access. These include a network-based intrusion detection system (NIDS) that constantly screens network traffic for hostile and suspicious behaviour in order to ensure the confidentiality, integrity, and availability of the data being transmitted over the Internet. However, researchers have made huge efforts to improve detection accuracy while decreasing False Alarm Rates (FAR) and to detect novel incursions, but they still confront difficulties. There has been an increase in the use of IDS systems based on machine learning and deep learning (ML and DL) in recent years. The study’s main goal is to detect attacks using a hybrid ML technique, and essential traits are identified via the Crow Search Algorithm (CSA). In order to improve classification accuracy, the decision tree is mapped to the Extreme Learning Machine (ELM). The decision tree selects features that are good at classifying. Additional weights for the selected features are calculated to improve their categorization accuracy. On the KDDCup dataset, the experiment is carried out in terms of a variety of parameter metrics.