Metaheuristics with federated learning enabled intrusion detection system in Internet of Things environment

Because of increased applications of Internet of Things (IoT) environment in real-time environment, confidential data gathered by the IoT devices are being communicated to the cloud environment to train the machine learning (ML) models in understanding the patterns that exist in the data. At the same time, the sensitive nature of the IoT data attracts malicious users into hacking efforts. An intrusion detection system (IDS) can be applied to ensure security in the IoT environment. In order to improve security, the ML models can be executed at the data source instead of centralized cloud server. Federated learning (FL) is a recent progression of ML model which enables the ML models to move into the data source rather than moving the data to centralized cloud and thereby resolves cybersecurity problems in the IoT environment. In this view, this study introduces an FL based IDS using bird swarm algorithm based feature selection with classification (FLIDS-BSAFSC) model in IoT environment. The presented FLIDS-BSAFSC model undergoes training on multiple aspects of IoT dataset in a decentralized format to classify, detect, and defend against attacks. The proposed FLIDS-BSAFSC model initially applies min–max normalization technique to pre-process the IoT data. Besides, BSA based feature selection (BSA-FS) technique is designed to elect feature subsets. Finally, social group optimization algorithm with kernel extreme learning machine model is employed for identifying various kinds of classes. In the view of FL where the IoT dataset is not distributed to the server carries out profile aggregation competently with the advantage of peer learning. The experimental validation of the FLIDS-BSAFSC model is tested using benchmark datasets and the results are inspected under several aspects. The experimental values highlighted the better performance of the FLIDS-BSAFSC model over recent approaches.