Evaluated bird swarm optimization based on deep belief network (EBSO-DBN) classification technique for IOT network intrusion detection

Abstract

Because of the recent development of various intrusion detection systems (IDS), which defend computer networks from security as well as privacy threats. The confidentiality, integrity and also availability of data may be compromised in the case that IDS prevention efforts fail. The amount of private, delicate and crucial data travelling over the worldwide network has expanded tremendously as a result of the recent development of Internet of Things (IoT) devices. Developing a better edge-based feature selection strategy, a deep learning technique for identifying and blocking malicious traffic, is the goal of intrusion detection. The classification method Evaluated Bird Swarm Optimization based Deep Belief Network (EBSO-DBN) has shown to be the most successful in this study. A variation of performance criteria have been used to critically assess deep learning techniques for IDS (accuracy, precision, recall, f-1 score, false alarm rate and detection rate). To ascertain the optimal performance of IDS models, this study focuses on building an ensemble classifier utilizing the suggested EBSO-DBN classification algorithm with 98.7% of accuracy, 99.4% of precision and 98.8% of recall.