Networking and Information Security Algorithms of Industrial Automation Control Systems

Computer technology and network technology have deeply penetrated into various fields such as life, production, and learning, becoming indispensable technological cornerstones in the 21st century. Industrial automation control systems have rapidly advanced and gained widespread adoption, thanks to robust information exchange and computer technology. Yet, as the informatization level of these systems rises, security risks in industrial control networks have become increasingly prominent, jeopardizing system stability and data security. To tackle these challenges, this paper introduces a deep learning (DL) -based information security algorithm tailored for industrial control networks. This algorithm monitors and analyzes network traffic in real-time, leveraging DL models to thoroughly analyze extensive network data and precisely detect potential attack behaviors. Experimental results demonstrate the algorithm's high accuracy, real-time capabilities, and effectiveness in identifying various network attacks. In comparison to traditional security measures, our algorithm excels in detection rate and false alarm rate, offering a novel approach to safeguarding industrial control networks.