GRU Enabled Intrusion Detection System for IoT Environment with Swarm Optimization and Gaussian Random Forest Classification

In recent years, machine learning (ML) and deep learning (DL) have significantly advanced intrusion detection systems, effectively addressing potential malicious attacks across networks. This paper introduces a robust method for detecting and categorizing attacks within the Internet of Things (IoT) environment, leveraging the NSL-KDD dataset. To achieve high accuracy, the authors used the feature extraction technique in combination with an autoencoder, integrated with a gated recurrent unit (GRU). Therefore, the accurate features are selected by using the cuckoo search algorithm integrated particle swarm optimization (PSO), and PSO has been employed for training the features. The final classification of features has been carried out by using the proposed RF-GNB random forest with the Gaussian Na & iuml;ve Bayes classifier. The proposed model has been evaluated and its performance is verified with some of the standard metrics such as precision, accuracy rate, recall F1-score, etc., and has been compared with different existing models. The generated results that detected approximately 99.87% of intrusions within the IoT environments, demonstrated the high performance of the proposed method. These results affirmed the efficacy of the proposed method in increasing the accuracy of intrusion detection within IoT network systems.