Designing Framework for Intrusion Detection in IoT Based on Spotted Hyena-Based ANN

Abstract

Internet of Things (IoT) is the platform for resource sharing amidst the computing platforms, mainly memory, power, large data, and storage. Securing the data in the IoT has become a prime concern as the hackers can access through the invaluable information from the IoT database. Once the attacks are incurred on the IoT environment, the loss of data is inevitable, and it can have a major effect on the progress of the IoT platform. In this paper, an Intrusion Detection System (IDS) with improved artificial intelligence is proposed. This paper makes use of standard benchmark datasets from diverse sources for performing the experiment. A well-performing machine learning algorithm called Artificial Neural Network (ANN) is developed with improved network architecture. The usage of a renowned meta-heuristic algorithm called Spotted Hyena Optimization (SHO) is used for selecting the optimal hidden neurons for ANN. The main objective of the improved training is to reduce the error difference between the target and the measured outputs to enhance the detection accuracy. Finally, the experimental outcomes and simulations prove the stability and robustness of the proposed model in terms of a variety of performance metrics over other machine learning models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.