A novel intrusion detection framework for industrial IoT: GCN-GRU architecture optimized with ant colony optimization

Abstract

The swift proliferation of IIoT ecosystems has highlighted the essential requirement for effective Intrusion Detection System (IDS) to protect crucial infrastructures. This research presents a novel hybrid IDS that combines Graph Convolutional Networks (GCN) with Gated Recurrent Units (GRU), optimized by the Ant Colony Optimization (ACO) method, a bio-inspired meta-heuristic based on ant foraging behavior. This method automates hyperparameter adjustment, overcoming the constraints of conventional human optimization techniques. The proposed system utilizes GCN for structural feature extraction and GRU for sequential pattern analysis, facilitating thorough anomaly detection in IIoT traffic. The ACO-optimized IDS surpasses traditional optimization methods, including Genetic Algorithms, by attaining quicker convergence and enhanced performance metrics. Notwithstanding its effectiveness, the computational burden of the optimization approach necessitates additional enhancement. Experimental assessments of the EDGE-IIOTSET, CICAPT-IIoT, and WUSTL-IIoT datasets reveal detection accuracies of 97 % for the majority of attack scenarios, alongside improved scalability and diminished processing requirements. This study emphasizes the capability of integrating sophisticated neural architectures with nature-inspired optimization to enhance Industrial Internet of Things (IIoT) security against advancing cyber threats.