Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

Internet of Everything (IoE), the recent technological advancement, represents an interconnected network of people, processes, data, and things. In recent times, IoE gained significant attention among entrepreneurs, individuals, and communities owing to its realization of intense values from the connected entities. On the other hand, the massive increase in data generation from IoE applications enables the transmission of big data, from context-aware machines, into useful data. Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems (IDS). In this background, the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System (IMVO-DLIDS) for IoT environment. The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment. The proposed IMVO-DLIDS model follows a three-stage process. At first, data pre-processing is performed to convert the actual data into useful format. In addition, Chaotic Local Search Whale Optimization Algorithm-based Feature Selection (CLSWOA-FS) technique is employed to choose the optimal feature subsets. Finally, MVO algorithm is exploited with Bidirectional Gated Recurrent Unit (BiGRU) model for classification. Here, the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model. The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct measures. An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.