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

ABSTRACT 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%.