IIoT Attack Detection Using Genetic Algorithm-Optimized Neural Networks

Abstract

The Industrial Internet of Things (IIoT) is accelerating digital transformation in industrial sectors through connected sensors, edge devices, and data-driven automation; however, this connectivity also increases exposure to cyber threats that can disrupt safety-critical and mission-critical operations. This study addresses the need for effective IIoT intrusion detection by proposing a hybrid model that combines a genetic algorithm (GA) with a neural network (NN), in which the GA optimizes NN parameters to improve classification performance in complex traffic environments. The proposed approach is evaluated on the Edge-IIoTset dataset, a labeled IIoT intrusion detection benchmark that includes normal traffic and multiple attack families (e.g., DDoS, malware, and injection-related attacks), represented by heterogeneous feature types and characterized by pronounced class imbalance. Dimensionality reduction is applied using principal component analysis to reduce feature redundancy and improve learning efficiency. Experimental results show that the GA-optimized NN achieves up to 95% overall accuracy across the evaluated attack categories, with particularly strong discrimination for dominant attack groups. The findings indicate that evolutionary optimization can enhance the performance of NN-based IDSs and provide a flexible optimization framework for IIoT settings, thereby supporting the development of more resilient intrusion detection solutions for industrial networks.