BIEIDS: Bio-Inspired Ensemble Method for Intrusion Detection System
Intrusion detection systems (IDSs) take part in information protection by detecting and preventing malevolent computer network actions. For many years, intrusion detection has been a prominent subject of study, and various intrusion detection systems have been cited in the literature. In this research work, a bio-inspired ensemble method is proposed for the intrusion detection system. IDS usually manages large quantities of data traffic containing redundant and inappropriate features that have an unhelpful effect on the IDS's performance. Using various dimensionality reduction techniques, unnecessary and improper features in the network traffic data are first eliminated using bio-inspired ensemble feature selection. Particle swarm optimization, Binary cuckoo search, and Fish swarm optimization algorithm are used to remove irrelevant features and select optimized features. The bagging and boosting ensemble classification model are built on the features chosen to detect the intrusion. The model and algorithms were simulated and tested using the available real-time datasets. The experimental results indicate that the proposed model can increase the detection rate and reduce the false alarm rate efficiently.