相关论文: A Physics-Based Attack Detection Technique in Cybe…

The smart grid combines the classical power system with information technology, leading to a cyber-physical system. In such an environment the malicious injection of data has the potential to cause severe consequences. Classical…

False data injection attacks (FDIAs) pose a persistent challenge to AC power system state estimation. In current practice, detection relies primarily on topology-aware residual-based tests that assume malicious measurements can be…

This paper studies the performance and resilience of a linear cyber-physical control system (CPCS) with attack detection and reactive attack mitigation in the context of power grids. It addresses the problem of deriving an optimal sequence…

This paper studies the vulnerability of phasor measurement units (PMUs) to false data injection (FDI) attacks. Prior work demonstrated that unobservable FDI attacks that can bypass traditional bad data detectors based on measurement…

Recent years have witnessed a rise in the frequency and intensity of cyberattacks targeted at critical infrastructure systems. This study designs a versatile, data-driven cyberattack detection platform for infrastructure systems…

A class of data integrity attack, known as false data injection (FDI) attack, has been studied with a considerable amount of work. It has shown that with perfect knowledge of the system model and the capability to manipulate a certain…

Increased interconnectivity of Cyber-Physical Systems, by design or otherwise, increases the cyber attack surface and attack vectors. Observing the effects of these attacks is helpful in detecting them. In this paper, we show that many…

In this paper a novel set-theoretic control framework for Networked Constrained Cyber-Physical Systems is presented. By resorting to set-theoretic ideas and the physical watermarking concept, an anomaly detector module and a control…

False Data Injection (FDI) attacks against powersystem state estimation are a growing concern for operators.Previously, most works on FDI attacks have been performedunder the assumption of the attacker having full knowledge ofthe underlying…

False data injection (FDI) cyber-attacks on power systems can be prevented by strategically selecting and protecting a sufficiently large measurement subset, which, however, requires adequate cyber-defense resources for measurement…

This paper outlines a cyber-physical authentication strategy to protect power system infrastructure against false data injection (FDI) attacks. We demonstrate that it is feasible to use small, low-cost, yet highly attack-resistant security…

This paper studies false data injection (FDI) attacks against phasor measurement units (PMUs). As compared to the conventional bad data detector (BDD), an enhanced BDD utilizing the effect of zero injection buses is proposed. Feasible…

One salient feature of cooperative formation tracking is its distributed nature that relies on localized control and information sharing over a sparse communication network. That is, a distributed control manner could be prone to malicious…

Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets.…

Although adverse effects of attacks have been acknowledged in many cyber-physical systems, there is no system-theoretic comprehension of how a compromised agent can leverage communication capabilities to maximize the damage in distributed…

This letter proposes a novel, fully distributed, transient-safe resilient secondary control strategies for AC microgrids, addressing unbounded false data injection (FDI) attacks on control input channels. Unlike existing methods that focus…

False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Detection of stealthy FDI attacks is impossible by the current bad data detection systems. Machine learning is one of the alternative methods…

We present a framework for bridging the gap between sensor attack detection and recovery in cyber-physical systems. The proposed framework models modern-day, complex perception pipelines as bipartite graphs, which combined with anomaly…

As cyber-physical systems (CPSs) become more dependent on data and communication networks, their vulnerability to false data injection (FDI) attacks has raised significant concerns. Among these, stealthy attacks, those that evade…

This paper is concerned with cyberattack detection in discrete-time, leader-following, nonlinear, multi-agent systems subject to unknown but bounded (UBB) system noises. The Takagi-Sugeno (T-S) fuzzy model is employed to approximate the…