Enhancing intrusion detection in IoT systems: A hybrid metaheuristics-deep learning approach with ensemble of recurrent neural networks

Abstract

Significant benefits have been brought to various industries by IoT (Internet of Things), but new security challenges have also been introduced due to the sheer volume and complexity of IoT systems. The protection of IoT systems from attacks and the assurance of their security posture are ensured by intrusion detection systems (IDSs). Recently, machine learning (ML) strategies have been broadly adopted for IDSs in IoT systems. However, there is still room for improvement, particularly in addressing the physical and functional diversity of IoT systems. A hybrid metaheuristics-deep learning approach is proposed in this paper for enhancing intrusion detection in IoT systems. An advanced metaheuristics algorithm with an ensemble of recurrent neural networks (RNNs) can be utilized to enhance the intrusion detection in IoT. Different types of attacks in IoT systems are identified by employing LSTM and GRU models, which constitute the RNNs. Feature selections are performed using Harris hawk optimization and fractional derivative mutation, as employed in this paper. To assess the proposed approach, publicly available datasets were utilized, and the empirical analysis demonstrated that the proposed approach works well than the other related methods in terms of accuracy and efficiency. Overall, the proposed work presents a promising solution for enhancing intrusion detection in IoT systems, and it has the potential to serve as a foundation for future research in this field.