Published Paper Details:

The worldwide expansion of the Internet of Things (IoT) has been characterized by significant growth, as seen by the widespread use of diverse devices in domains such as housing environments, transportation systems, healthcare facilities and industrial sectors. The introduction and integration of the IoT concept in industrial environments has resulted in significant modifications to the architecture of Industrial Automation and Control Systems (IACS), as well as the widespread interconnectivity of various industrial systems. The outcome of this development is often known as the Industrial IoT(IIoT), which eliminates the obstacle of linking IACS with separate traditional Information and Communication Technology (ICT) platforms. In contemporary times, the IoT has begun to affect upon our individual lives and extend its influence beyond our immediate surroundings, therefore establishing a foundation for imminent cyber-attacks targeting the IoT. The extensive utilization of the IoT has produced a productive ground for potential assaults against IoT systems. Machine learning (ML) algorithms have been used as effective tools for enhancing the security of wireless communication in IIoT-based systems, as well as addressing a range of cyber security issues. Hence, this study presents an improved ensemble model for intrusion detection that utilizes Firefly Algorithm (FA) to classify hostile behaviors in network data within the context of IIoT. The performance of the proposed model was evaluated usingprecision, recall, F1 score,accuracy, F2 score and ROC-AUC metrics on the X-IIoTIDDataset, which is an IIoT-based cyber security dataset. The obtained findings were compared to those of other recent state-of-the-art ML models, and it was observed that our model exhibited superior performance.