Intrusion detection system using metaheuristic fireworks optimization based feature selection with deep learning on Internet of Things environment

Internet of Things (IoT), cloud computing, and other significant advancements in communication have created new security challenges. Due to these advancements and the ineffectiveness of the current security measures, cyber-attacks are also increasing quickly. Recently, several artificial intelligence (AI)-based solutions have been presented for various secure applications, such as intrusion detection. This article proposes an intrusion detection system using dynamic search fireworks optimization-based feature selection with optimal deep recurrent neural network (DFWAFS-ODRNN) model in IoT environment. The presented DFWAFS-ODRNN model follows a two-stage process, namely, feature selection and intrusion classification. In the first phase, the DFWAFS-ODRNN model elects an optimal subset of features using the dynamic search fireworks optimization algorithm (DFWAFS) technique. Next, in the second stage, the intrusions are identified and categorized using the DRNN model. At last, the hyperparameters of the DRNN are optimally chosen by the Nadam optimizer. A detailed simulation analysis of the DFWAFS-ODRNN model is validated on benchmark intrusion detection system (IDS) dataset, and the outcomes show the efficacy of intrusion detection. The proposed model efficiently detects the intrusion detection with an accuracy of 96.11%.