Open Access

ARTICLE

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

Mohammad Shoab^*, Loiy Alsbatin^*

Department of Computer Science, College of Science and Humanities Al Dawadmi, Shaqra University, Al Dawadmi, 17441, Saudi Arabia

* Corresponding Authors: Mohammad Shoab. Email: ; Loiy Alsbatin. Email:

Computers, Materials & Continua 2024, 81(1), 625-642. https://doi.org/10.32604/cmc.2024.053721

Received 08 May 2024; Accepted 20 August 2024; Issue published 15 October 2024

Abstract

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 auto-encoder, 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 Naïve 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.

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Keywords

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