This paper proposes a honeypot-driven active detection framework to address the growing cyber threats targeting smart grids. To address the limitations of existing detection methods and the lack of proactive defense capabilities, the framework integrates interpretable decision mechanisms with an adaptive modular architecture, through the dynamic weighting of the gated aggregation mechanism. It incorporates data preprocessing, honeypot interaction, dual-path detection, and decision fusion to more effectively capture diverse attack behaviors and strengthen overall detection reliability. The framework integrates a GBDT-based anomaly scorer with a dedicated Denial-of-Service (DoS) detector, and combines multidimensional evidence using a gated aggregation mechanism. Moreover, theoretical analyses establish a lower bound on feature distinguishability, prove model convergence, and demonstrate effective false-alarm suppression, providing formal guarantees for feasibility. Finally, experiments on real-world smart grid datasets show that the proposed framework accurately detects DoS, replay, and integrity attacks while maintaining strong overall performance. The results confirm its effectiveness as a theoretically grounded and proactive defense solution for smart grid security.

This article investigates the issue of distributed state estimation for a nonlinear hydraulic system. Our objective is to propose a distributed algorithm that enables individual nodes to obtain a global state estimation by utilizing local measurements and information gathered from neighboring nodes. We ensure global optimality by designing a mechanism for information exchange between neighboring nodes. The state estimation acquired by our distributed algorithm is equivalent to that of the centralized extended Kalman filter within a finite time. Our proposed algorithm is effective for acyclic network graphs and can be applied to cyclic network graphs through the loop removal approach. In addition, we provide a stability analysis of the proposed distributed algorithm. Finally, experiments on the practical platform are conducted to verify the feasibility and merits of the proposed algorithm.

The secure operation of electricity generation system (EGS) is essential to the quality of electricity and the security of energy Internet. In order to design secure control strategy for EGS, the behavior and capacity of adversary and possible attack should be studied. From this point of view and considering the actuator saturation and the false-data injection attack in control input channel of EGS, we analyze the behavior and stealthiness of adversary and design stealthy attack strategies in this paper. First, we prove that the magnitude of effective attack signal is smaller than the magnitude of injected attack signal because of actuator saturation. Second, we find that smaller actuator saturation limit makes the adversary easier to be detected. Finally, two stealthy attack strategies are designed for EGS which incorporates a χ2attack detector, and the effectiveness of them are illustrated through simulation.

Despite of its wide success in many distributed statistical learning applications, the well-known Gaussian belief propagation (BP) algorithm still lacks sufficient understanding at the theoretical level. This paper studies the convergence of Gaussian BP by analyzing the dynamic behaviour of the marginal covariances. We show, under a mild technical assumption, that the information matrices (i.e., the inverses of marginal covariances) are guaranteed to converge exponentially to positive-definite matrices. The convergence rate is explicitly characterized. This result is a key step to the understanding of the dynamic behaviour of the BP iterations.