Local Cyber-Physical Attack for Masking Line Outage and Topology Attack in Smart Grid

Chung, Hwei-Ming; Li, Wen‐Tai; Yuen, Chau; Chung, Wei-Ho; Zhang, Yan; Wen, Chao-Kai

doi:10.1109/tsg.2018.2865316

Cited by 81 publications

(37 citation statements)

References 34 publications

(52 reference statements)

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“…With further research, attack strategies have been improved, for example, optimal FDI attack actions are studied to mask the exceptional frequency leading to the largest disruption of generators in the work by the authors of [16]. In the work by the authors of [17], FDI attacks with limited knowledge are described to mask the line outages. In the work by the authors of [18], time synchronization attack was introduced.…”

Section: Fdi Attackmentioning

confidence: 99%

“…False command injection caused the system failure and FDI could delay the time of attack detection and develop impact of attacks. Based on the above discussion, the characters of FDI attacks [13][14][15][16][17][18][19] are summarized in Table 4. We can clearly see that the FDI attack becomes more complex, hides the traces of attacks better, and causes greater disruption.…”

Section: Fdi Attackmentioning

confidence: 99%

“…Previous works [13][14][15][16][17][18][19] have extensively studied FDI attacks. However, previous FDI attacks mainly pay attention to masking the wrong system state or disturbing the state estimation by launching just one attack.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advanced Bad Data Injection Attack and Its Migration in Cyber-Physical Systems

Deng

Yang²,

Xun

et al. 2019

Electronics

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False data injection (FDI) attack is a hot topic in cyber-physical systems (CPSs). Attackers inject bad data into sensors or return false data to the controller to cause the inaccurate state estimation. Although there exists many detection approaches, such as bad data detector (BDD), sequence pattern mining, and machine learning methods, a smart attacker still can inject perfectly false data to go undetected. In this paper, we focus on the advanced false data injection (AFDI) attack and its detection method. An AFDI attack refers to the attack where a malicious entity accurately and successively changes sensory data, making the normal system state continuously evaluated as other legal system states, causing wrong outflow of controllers. The attack can lead to an automatic and long-term system failure/performance degradation. We first depict the AFDI attack model and analyze limitations of existing detectors for detecting AFDI. Second, we develop an approach based on machine learning, which utilizes the k-Nearest Neighbor (KNN) technique and heterogeneous data including sensory data and system commands to implement a classifier for detecting AFDI attacks. Finally, simulation experiments are given to demonstrate AFDI attack impact and the effectiveness of the proposed method for detecting AFDI attacks.

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Section: Fdi Attackmentioning

confidence: 99%

Section: Fdi Attackmentioning

confidence: 99%

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Advanced Bad Data Injection Attack and Its Migration in Cyber-Physical Systems

Deng

Yang²,

Xun

et al. 2019

Electronics

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“…In CPPS, that abnormal monitoring and control functions in the cyber network expand the scale of the large-scale blackout in [7][8]. Malicious information attacks on cyber networks aggravate the failures of coupled networks in [9][10]. Through the coupled effect inside the grid, generates interactive propagation cascading failure in the network through the coupling between systems, the failures of a single network evolve into cascading failures interactively propagating between cyber networks and power systems, and then enlarge the scale of the blackout.…”

Section: Introductionmentioning

confidence: 99%

Dual Hidden Failure Model for Cyber Physical Power System

Gong³

et al. 2020

IEEE Access

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With the increasingly close coupling between the cyber network and the physical power grid in Cyber Physical Power System (CPPS), the failure of cyber network has become an important cause of blackout. Equipment failure or abnormal operation state will be appeared without alarm in complex cyber network, which brings hidden failures not been diagnosed and dealt with in time. The invisibility and harmfulness of hidden failures pose a significant threat to the safe, reliable, and stable operation of CPPS. The dual hidden cascading failure model for CPPS is proposed to reveal the failure characteristics of CPPS accurately, considering not only the hidden faults in the power grid, but also the hidden failures in the cyber network. With the different coupled relationships between power systems and cyber networks, the cyber network topology with corresponding failure threshold is obtained according to the simulation of the model with different hidden failure rates of cyber network nodes. When the hidden failure rate is lower than the failure threshold, the scale-free topology cyber network coupled mode is adopted between power grids and cyber networks, otherwise the small-world topology cyber network coupled mode is adopted, which can effectively reduce the risk of power system blackouts. The method proposed in this paper is tested by using IEEE-118 power system and its cyber network. The results show that the method is effective. INDEX TERMS Cascading failure, Complex networks, Cyber physical power system, Hidden failure, Vulnerability.

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“…Ref. [24]- [26] analyzed the impacts of cyber-attacks on power grids vulnerability to cascading outages, and Ref. [27], [28] studied mitigation and recovery approaches from cascading failures.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of Cascading Outages Using a Dynamic Interaction Graph-Based Optimal Power Flow Model

Chen

Sun

et al. 2019

IEEE Access

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Online decision support for effective mitigation actions against propagating cascading outages in a power grid still poses a big challenge to grid operators. This paper proposes an online mitigation strategy against cascading outages using an optimal power flow model based on a Dynamic Interaction Graph. For a given power grid, its interaction graph, also called an interaction model, was proposed in recent literature, which is composed of components and links that have large contributions to outage propagation. Differing from a conventional interaction graph, a Dynamic Interaction Graph can adaptively update its parameters with changes of grid topology and hence is more accurate in online identification of the most vulnerable transmission lines and likely outage propagation paths for mitigation. Further, the paper introduces an optimal power flow model based on the proposed Dynamic Interaction Graph for determining the optimal control strategy to maintain transfer margins of vulnerable transmission lines and effectively mitigate outage propagation. The numerical results on the IEEE 118-bus system demonstrate the effectiveness of the proposed models and associated control strategy.

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Local Cyber-Physical Attack for Masking Line Outage and Topology Attack in Smart Grid

Cited by 81 publications

References 34 publications

Advanced Bad Data Injection Attack and Its Migration in Cyber-Physical Systems

Advanced Bad Data Injection Attack and Its Migration in Cyber-Physical Systems

Dual Hidden Failure Model for Cyber Physical Power System

Mitigation of Cascading Outages Using a Dynamic Interaction Graph-Based Optimal Power Flow Model

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