Methods for blocking malicious traffic with static Bayesian game in IIoT

With the development of the Industrial Internet of Things (IIoT), its security issues have become prominent, and network attacks have continued to increase, including malicious traffic threats. There are already many effective methods for detecting malicious traffic in the Industrial Internet of Things, but how to handle detected malicious traffic lightly and effectively? Based on this problem, we propose a method to dynamically adjust malicious traffic in the Industrial Internet of Things using Software-Defined Networking (SDN). With the help of SDN's programmability of the net-work and the characteristics of decoupling the control plane and the data plane, through the SDN controller OpenFlow rule entries corresponding to malicious traffic are generated, and then the SDN switch updates the flow table to achieve the purpose of blocking malicious traffic. At the same time, we consider two types of known types of malicious traffic and unknown types of malicious traffic. Different strategies of traffic blocking, including traffic dropping and traffic redirection, conduct a static Bayesian game be-tween two types of malicious traffic and two traffic blocking strategies, taking into account factors such as current and future benefits, response costs, and risk levels, through the Harsanyi transformation reasoning proves that the Nash equilibrium point and equilibrium strategy are found, and then the strategy is numerically analyzed and experimentally verified. The final result is that when the known type of malicious traffic is discarded and the unknown type of malicious traffic is redirected, comprehensive maximum utility.