Industrial Control System(ICS) testbed is the basis of ICS safety research. In order to build an ICS testbed, it is necessary to have a deep understanding of the current research status. This paper introduced the structure and composition of the typical ICS system. Then it simply analyzed the technical characteristics, system framework and advantages and disadvantages of four types of typical ICS testbeds. Finally, it summarized application scenarios of ICS testbed and pointed out some problems in testbed construction and the development direction of ICS testbed with the fusion of cyber and physical.

With the convergence of computer technology and industrial networks, attackers are not limited to attacking only individual users’ computers, turning to attack industrial control systems that can cause major infrastructure problems. Programmable Logic Controllers (PLC) are the core components of industrial control systems. Its safety has a profound impact on the safety of the entire industrial system. This paper firstly classifies the security research of PLC according to the structure and function, and expounds the existing security defects of PLC from the aspects of firmware security, operation security and program security. Then it summarizes and analyzes four types of security protection measures: the integrity of verification firmware, protocol security encryption, code formal verification, and program security defence detection. Finally, according to the overall safety of the industrial system and the actual development of the current PLC, we discuss the development trend of safety research.

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In recent years, with the upgrading of the attack technology, stealthy DoS jamming attacks have become the primary factor to threaten the security of Industrial Cyber-Physical Systems (ICPS). Considering the complex industrial scenarios of ICPS, which are influenced by a variety of external and internal interference, a H∞ controller designing problem is studied in this paper for an ICPS which deploys a hybrid-triggered mechanism (HTM) in the wireless channel encountering stealthy DoS jamming attacks. By employing a compensation mechanism which is employed in the controller to mitigate the impacts of attacks, external disturbance, limited channel capacity, wireless channel noise, we establish a closed-loop system and prove the closed-loop system is mean square exponentially stable and can achieve the desired H∞ disturbance rejection level theoretically. Finally, simulation examples are used to demonstrate effectiveness of the proposed H∞ controller.

In industrial control systems (ICSs), the PROFIBUS-DP (decentralized peripherals) protocol is widely used for communication between devices. Because PROFIBUS-DP is an unencrypted and insecure bus protocol, attackers can connect to the PROFIBUS-DP system and arbitrarily manipulate I/O process values, which may interrupt the normal operation of industrial equipment, or have more serious consequences. However, due to the complex structures of bus networks and the large number of attack areas, the existing scheme does not monitor all the messages in the industrial head office network and cannot effectively detect semantic attacks. To solve this problem, we propose a novel attack detection system DpGuard. DpGuard automatically builds a finite-state machine model of normal ICS behavior through a large number of historical ICS traffic data. The model includes state events, state transitions, state transition probability, and other normal behavior information. In addition, DpGuard records the execution status of the context data package, uses the real-time captured data package as the input of the model, and judges whether the state event and state transition probability conform to the constraints of the finite-state machine model, so as to identify the legitimate normal behavior of the ICS. Our proposal was evaluated using two Siemens PLCs (programmable logic controllers) deployed on the PROFIBUS-DP system. The experimental results demonstrated that the scheme could accurately detect fault injection and semantic attacks. Compared with other detection models, our scheme presented an improved detection performance, with a detection accuracy of 99.80%.

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