Abstract: Electricity refers to energy powered by electrical energy. The complete power system includes power generation, transmission, substation, power distribution and power consumption. Electricity is a basic industry that affects the national economy and the people’s livelihood. Power supply and security are related to national security strategies and are related to the overall situation of economic and social development. Industrial automation and control systems (referred to as “industrial control”) as the sensory and central nervous system of electricity, to ensure their network security, so that it is always in a stable and reliable state of operation, is essential to ensure safe operation of electricity. Because most networks are highly interconnected, they are vulnerable to cyber attacks. Although network-based intrusion detection systems can combine intrusion warnings and security responses well, as technology continues to evolve, attacks become more common and difficult to detect, and escape technology is a representative of such technologies. It can evade detection by the intrusion detection system by masquerading the network data stream. Combining with the knowledge and the characteristics of the power industrial control network, a power industrial network intrusion detection system based on deep reinforcement learning is proposed. The deep reinforcement learning algorithm combines the neural network and Q-learning methods into the network. The anomaly is trained to enable the system to detect intrusions and issue warnings in a timely manner.

Key words: electric power industrial control network, network intrusion, neural network, DQN