Secure control for cyber–physical power systems (CPPSs) under cyber attacks is a challenging issue. Existing event-triggered control schemes are generally difficult to mitigate the impact of cyber attacks and improve communication efficiency simultaneously. To solve such two problems, this article studies secure adaptive event-triggered control for the CPPSs under energy-limited denial-of-service (DoS) attacks. A new DoS-dependent secure adaptive event-triggered mechanism (SAETM) is developed, where DoS attacks are taken into account when designing the trigger mechanisms. Sufficient conditions are derived to ensure the CPPSs to be uniformly ultimate boundedness stable, and the entering time when the state trajectories of the CPPSs are guaranteed to stay in the secure region is also given. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed control method.

Persistent data packet losses induced by consecutive denial-of-service (DoS) attacks could fail traditional state estimation (SE) algorithms that highly rely on the completeness of dataset. To solve the problem, this article explores a novel SE algorithm with enhanced SE accuracy for power systems against consecutive DoS attacks. First, according to the characteristics of data packet losses induced by DoS attacks, we design a strategy by using the latest received measurement packet to compensate for consecutive data packet losses, and reconstruct the power system model. Second, by integrating Holt’s two-parameter exponential smoothing and extended Kalman filter techniques, a new enhanced SE algorithm is proposed, where the statistical properties of data packet losses are contained in the recursion formulas of the state prediction and state filtering processes. Third, the boundedness of estimation error covariance matrix and prediction error are proved. Finally, the proposed algorithm is compared with traditional SE algorithms via three IEEE testing systems and verified in a real power system. Simulation results illustrate the effectiveness and efficiency of the proposed algorithm under various data packet losses scenarios.

We here investigate the secure control of networked control systems developing a new dynamic watermarking (DW) scheme. First, the weaknesses of the conventional DW scheme are revealed, and the tradeoff between the effectiveness of false data injection attack (FDIA) detection and system performance loss is analyzed. Second, we propose a new DW scheme, and its attack detection capability is interrogated using the additive distortion power of a closed-loop system. Furthermore, the FDIA detection effectiveness of the closed-loop system is analyzed using auto/cross-covariance of the signals, where the positive correlation between the FDIA detection effectiveness and the watermarking intensity is measured. Third, the tolerance capacity of FDIA against the closed-loop system is investigated, and theoretical analysis shows that the system performance can be recovered from FDIA using our new DW scheme. Finally, the experimental results from a networked inverted pendulum system demonstrate the validity of our proposed scheme.

Power systems usually employ bad data detection (BDD) to avoid faulty measurements caused by their anomalies, and hence can ensure the security of the state estimation of power systems. However, recently BDD has been found vulnerable to malicious data deception attacks submerged in big data. Such attacks can purposely craft sparse measurement values (i.e. attack vectors) to mislead power estimates, while not posing any anomalies to the BDD. Some related work has been proposed to emphasize this attack. In this paper, a new malicious data deception attack by considering a practical attacking situation is investigated, where the attacker has limited resources for corrupting measurements. In this case, attackers generate attack vectors with less sparsity to evade conventional BDD, while using a convex optimization method to balance the sparsity and magnitude of attack vectors. Accordingly, the effects of such an attack on operational costs and the risks of power systems are analysed in detail. Moreover, according to security evaluation for individual measurements, such attacks can be detected with high probability by just securing one critical measurement. Numerical simulations illustrate the effectiveness of the proposed new attack case and its detection method.

Low light very likely leads to the degradation of an image's quality and even causes visual task failures. Existing image enhancement technologies are prone to overenhancement, color distortion or time consumption, and their adaptability is fairly limited. Therefore, we propose a new single low-light image lightness enhancement method. First, an energy model is presented based on the analysis of membrane vibrations induced by photon stimulations. Then, based on the unique mathematical properties of the energy model and combined with the gamma correction model, a new global lightness enhancement model is proposed. Furthermore, a special relationship between image lightness and gamma intensity is found. Finally, a local fusion strategy, including segmentation, filtering and fusion, is proposed to optimize the local details of the global lightness enhancement images. Experimental results show that the proposed algorithm is superior to nine state-of-the-art methods in avoiding color distortion, restoring the textures of dark areas, reproducing natural colors and reducing time cost. The image source and code will be released at https://github.com/leixiaozhou/CDEFmethod.