With the rapid development of communication, control, and computer technology, traditional power systems have evolved into cyber-physical power system (CPPS). However, CPPS not only affords convenience but also introduces more cyber-attacks. Among many types of cyber-attacks, false data injection attacks (FDIAs) have drawn much attention in the CPPS security domain due to their stealthiness. Most FDIAs are based on the static model on a single snapshot and often ignore the reality of dynamically time-evolving CPPS. Accordingly, this article proposes a novel three-stage dynamic false data injection attack (DFDIA) model in CPPS by considering potential dynamic behaviors. To consider both attack location and attack amplitude, the designing DFDIA is formulated as two constrained single-objective optimization problems. Two versions of constrained differential evolution are presented as the solver to determine the attack location and optimize the attack vector for collaboratively altering the meter measurements. Then, an interval state forecasting-based countermeasure is proposed to detect the established DFDIA. In this detector, the variation bounds of state values are determined via ensemble deep learning-based state forecasting method. Finally, extensive simulation results on several IEEE bus systems demonstrate the feasibility of DFDIA and the effectiveness of the defense mechanism.

In cyber-physical power systems (CPPSs), additional control as the compensating control plays a very important part in load frequency control (LFC) systems. The additional control in the LFC system is constrained by limited communication resources and cyber-attacks, which may cause performance degradation or destabilize the system. Accordingly, this paper proposes resilient event-triggered LFC for CPPSs with an additional control loop under denial-of-service (DoS) attacks. Firstly, a resilient event-triggered communication scheme is presented to reduce the occupation of communication resources under DoS attacks in the additional control loop. Then, different from existing event-triggered LFC systems, this paper establishes a novel switched LFC system model, where the resilient event generator is integrated into an additional control loop when suffering from DoS attacks. It is the first time that the additional control loop of the LFC system simultaneously considers communication resources and cyber-security. Furthermore, we derive exponential stabilization criteria by applying the Lyapunov stability theory based on the established model. Criteria are derived to obtain the weighting matrix and controller gain simultaneously by applying the linear matrix inequality technique. Finally, a one-area and two multi-area CPPSs with the additional control loop under DoS attacks are used to testify the availability of the proposed resilient event-triggered LFC scheme.

Industrial automation and control systems (IACS) are tremendously employing supervisory control and data acquisition (SCADA) network. However, their integration into IACS is vulnerable to various cyber-attacks. In this article, we first present population extremal optimization (PEO)-based deep belief network detection method (PEO-DBN) to detect the cyber-attacks of SCADA-based IACS. In PEO-DBN method, PEO algorithm is employed to determine the DBN's parameters, including number of hidden units and the size of mini-batch and learning rate, as there is no clear knowledge to set these parameters. Then, to enhance the performance of single method for cyber-attacks detection, the ensemble learning scheme is introduced for aggregation of the proposed PEO-DBN method, called EnPEO-DBN. The proposed detection methods are evaluated on gas pipeline system dataset and water storage tank system dataset from SCADA network traffic by comparing with some existing methods. Through performance analysis, simulation results show the superiority of PEO-DBN and EnPEO-DBN.