Review on Intelligent techniques for Network Intrusion Detection System
Networks performs a key role in current existence; network security has become a vital research place. An intrusion detection device (IDS)which is vibrant cyber security technique, monitors the state of software and hardware running in the network. Regardless of a long-time development, existing IDSs still face challenges in enhancing the detection accuracy, lowering the false alarm rate, and detecting unknown assaults. The Network Intrusion Detection System (NIDS) plays a crucial role in preserving information protection and especially classifying various attacks on contemporary networks. In the current situation, the option of an appropriate combination of anomaly detection features is more important in the NIDS. Swarm Intelligence (SI) techniques commonly used to select the features in the high dimensional dataset to improve the accuracy. Machine learning (ML) techniques exhibited high ability to develop the intrusion detection algorithm in the network field. Deep Learning techniques has been widely used to improve the performance on a NIDS to detect various network attacks. The implementation of an intelligent algorithm to resolve a wide range of NIDS issues is investigating namely the Swarm intelligence algorithm, machine learning algorithm, deep learning algorithm based on exploratory analysis to identify the benefit of using intrusion detection enhancement techniques.