Authors - Safwan Ahmed, Rojas Binny, S N Velukutty, Animesh Giri Abstract - Industrial Internet of Things (IIoT) networks have started to play a crucial role in revolutionizing manufacturing and Industry 4.0. However, the distributed nature of IIoT and its relative infancy make it a prime target for cyberattacks. This paper proposes a new approach to address the threats faced by industries using a conversational Artificial Intelligence (AI)-interfaced deep neural network model for detecting attacks on an IIoT network. The proposed approach extends to threat mitigation and has been evaluated using an extensive IIoT network traffic data set, demonstrating 93% accuracy in detecting 14 of the most common threats plaguing the industry. This model introduces the integration of conversational AI with deep learning, offering a user-friendly interface for naive users and accurate threat detection. The broader impact of this work lies in its potential to significantly enhance access to a robust and accurate real-time cyber-threat detection and mitigation system, thus contributing to a more secure and resilient industrial landscape.
