Xinge Li

5G industrial cyber–physical systems (5G-ICPSs) have attracted substantial research interests due to their capability in the interconnection of everything. However, integrating the 5G network may expose systems to more potential risks. To reveal attack propagation, an attack path prediction approach based on dual reinforcement learning (RL) is proposed. First, a dual-network model is established, incorporating the security constraints for attacks against the 5G network into the attack graph. Second, employing RL, $Q $ -value updating functions and reward mechanisms based on topology and vulnerability are designed. Finally, an optimal attack path prediction algorithm is developed. Unlike traditional methods, the proposed approach does not rely on the monotonicity assumption that a system component has only one vulnerability, enabling it to accurately predict the optimal attack paths. Our simulation results demonstrate that the proposed approach can identify possible attack sources and paths from a 5G-ICPS.

5G communication network has become a major pillar in the evolution of interconnected industrial systems. However, the introduction of 5G network may lead to unknown risks in the systems. To reveal the impact of network threats on 5G-based industrial systems, a 5G network security analysis approach combining formal modeling and attack penetration is proposed. Firstly, the 5G network models based on topology and transmission events are established to cope with diverse and hidden attack routes and behaviors. Then, the attack module is integrated into the network model. With attack penetration to the models, potential vulnerabilities are exploited and quantified based on the hierarchical-topology model, and network reliability is evaluated based on the transmission-event model. The simulation results identify and quantify network vulnerabilities under various attacks, including access authentication failure, destruction of data integrity, illegal control of Network Functions (NFs), and malicious consumption of shared slicing resources. Meanwhile, a more unpredictable outcome is that there is a threshold of access probability, a, to measure the impacts of attacks against the bearer network and core network on reliability. Finally, a practical case about the impact of network security on a 5G-based coupled-tank system is discussed, which further proves the feasibility of our approach.

Underwater optical wireless sensor networks (UOWSNs) have been attracting many interests for advantages of high transmission rate, ultra-wide bandwidth, and low latency. However, due to limited energy resources and highly-dynamic topology caused by water flow movement, it is challenging to provide a low-consumption and reliable routing in UOWSNs. To tackle this issue, in this paper, we propose an efficient routing protocol based on multi-agent reinforcement learning, termed as DMARL, for UOWSNs. The network is firstly modeled as a distributed multi-agent system, and residual energy and link quality are considered into the routing protocol design to improve the adaptation to a dynamic environment and the support of prolonging network life. Additionally, two optimization strategies are proposed to accelerate the convergence of the reinforcement learning algorithm. On the basis, a reward mechanism is provided for the distributed system. Simulation results show that DMARL-based routing protocol has low energy consumption and high packet delivery ratio (over 90%), and it is suitable for networks where the average number of neighbour nodes is less than 14.