IoT-RNNEI: An Internet of Things Attack Detection Model Leveraging Random Neural Network and Evolutionary Intelligence

Over the past few years, there has been significant research on the Internet of Things (IOT), with a major challenge being network security and penetration. Security solutions require careful planning and vigilance to safeguard system security and privacy. This paper proposes a new hybrid intrusion detection system (IDS) based on machine learning and metaheuristic algorithms, which has 3 stages: (1) Pre-processing, (2) feature selection, and (3) attack detection. In the pre-processing stage including, cleaning, visualization, feature engineering and vectorization. In the feature selection stage, a combined grasshopper optimization algorithm and sine–cosine algorithm is used; the modified grasshopper algorithm improves the performance of the grasshopper algorithm with a centralized population initialization in terms of search capability, convergence speed, and capacity to deviate from the local optimum. In the attack detection stage, a random neural network is used, and the modified grasshopper algorithm adjusts the structure and parameters of the random neural network. The proposed method is evaluated using the DS2OS datasets, CIC-IOT2023 and CIC-IDS2018. The results have shown that the proposed approach in these experiments, through a multiple learning model, resulted in an improvement in accuracy to 99.56%.