Research On Intrusion Response Technology Based On Reinforcement Learning

With the rapid development of Industrial Internet of Things(IIo T)technology,more and more industrial control systems(ICS)are connected to the internet,making them vulnerable to various cyber threats.A cyber attack or intrusion on an ICS system could result in serious consequences such as production line shutdown,equipment damage,data loss,or leakage,which could have a significant impact on industrial production and economic development.Therefore,ensuring the security of ICS systems is crucial,and intrusion response mechanisms should be considered as the last line of defense in the ICS security system.Only by taking fast and effective response measures when ICS intrusion incidents occur can ICS systems be protected from attacks and damage.This thesis analyzes and studies intrusion response mechanisms for ICS systems.The main contributions and innovations of this thesis are as follows:The thesis proposes an optimal security protection strategy selection model for industrial control systems.The model uses Q-Learning to optimize the parameters of the particle swarm algorithm,enabling the quick search of the optimal security protection strategy.Compared with traditional particle swarm algorithms,the Q-Learning-based algorithm is less likely to fall into local optima and can obtain better protection strategies,achieving the minimum attack cost and protection cost.The experiments show that this model has significant advantages in improving the security of industrial control systems.The thesis proposes a strategy selection model based on deep reinforcement learning to protect industrial control systems from attacks.This model uses the deep reinforcement learning algorithm,taking the network state as input and actions and rewards as output,to obtain the security protection strategy through continuous training.The experiments show that this model can meet the security protection requirements and quickly find the optimal security protection strategy after an attack.Moreover,the thesis optimizes the deep reinforcement learning algorithm to accelerate its convergence speed.