Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

Ten, Chee‐Wooi; Yamashita, Koji; Yang, Zhiyuan; Vasilakos, Athanasios V.; Ginter, Andrew

doi:10.1109/tsg.2017.2656068

Cited by 64 publications

(38 citation statements)

References 67 publications

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“…In [18], an attempt is conducted to investigate the impact on the cyber‐physical power grid in cases where communications and operations of distributed energy resources are manipulated by an adversary. In [19], research is conducted to assess the dynamic impacts of hypothesised cyber attacks on substations and concluded that the dynamic study is more capable in detecting cascading failure due to cyber attacks. However, Ten et al [19] only evaluate the dynamic impacts of one type of attack, which is the disrupt switching attack, and the dynamic impact analysis of other types are not covered.…”

Section: Introductionmentioning

confidence: 99%

Cyber attacks in smart grid – dynamic impacts, analyses and recommendations

Amin

Taghizadeh

Rahman³

et al. 2020

IET cyber-phys. syst.

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Cyber attacks can cause cascading failures and blackouts in smart grids. Therefore, it is highly necessary to identify the types, impacts and solutions of cyber attacks to ensure the secure operation of power systems. As a well-known practice, steady-state analysis is commonly used to identify cyber attacks and provide effective solutions. However, it cannot fully cover non-linear behaviours and cascaded blackouts of the system caused by dynamic perturbations, as well as provide a postdisturbance operating point. This study presents a novel approach based on dynamic analysis that excludes the limitations of the steady-state analysis and can be used in the events of various cyber attacks. Four types of common attacks are reviewed, and their dynamic impacts are shown on the IEEE benchmark model of the Western System Coordinating Council system implemented in MATLAB Simulink. Then, recommendations are provided to enhance the security of the future smart power grids from the possible cyber attacks.

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Section: Introductionmentioning

confidence: 99%

Cyber attacks in smart grid – dynamic impacts, analyses and recommendations

Amin

Taghizadeh

Rahman³

et al. 2020

IET cyber-phys. syst.

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“…f p = n tp n tp + n fp (7) f r = n tp n tp + n fn (8) where f p is the precision metric. f r is the recall metric.…”

Section: Anomaly Detection Methodsmentioning

confidence: 99%

“…The existence of cyber threats in CPS, such as cyber components failures, security risks, and cyber-attacks, can readily lead to the abnormal operation or even cascading outages of the entire power grid by propagating from a single point failure in cyber networks. For example, the ''Ukrainian Blackout'' [7] in 2015 was a typical cyber-physical cascading failure caused by malicious cyber-attacks, which aroused widespread concerns in the cyber security of power system operations.…”

mentioning

confidence: 99%

Multivariate Gaussian-Based False Data Detection Against Cyber-Attacks

Liu

2019

IEEE Access

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Modern distribution power system has become a typical cyber-physical system (CPS), where reliable automation control process is heavily depending on the accurate measurement data. However, the cyber-attacks on CPS may manipulate the measurement data and mislead the control system to make incorrect operational decisions. Two types of cyber-attacks (e.g., transient cyber-attacks and steady cyberattacks) as well as their attack templates are modeled in this paper. To effectively and accurately detect these false data injections, a multivariate Gaussian based anomaly detection method is proposed. The correlation features of comprehensive measurement data captured by micro-phasor measurement units (µPMU) are developed to train multivariate Gaussian models for the anomaly detection of transient and steady cyberattacks, respectively. A k-means clustering method is introduced to reduce the number of µPMUs and select the placement of µPMUs. Numerical simulations on the IEEE 34 bus system show that the proposed method can effectively detect the false data injections on measurement sensors of distribution systems. INDEX TERMS Cyber-physical system, cyber-attack, anomaly detection, distribution grid, machine learning. I. INTRODUCTION Information technology plays a critical role in the automatic control of modern distribution power systems, which consist of a large number of computation systems, local sensors, and communication networks. With the increasing and deep interaction between physical flow and cyber flow, the modern distribution system becomes a typical cyber-physical system (CPS) [1], [2]. The core of CPS in power grids is to achieve high-sensitivity awareness and real-time automation of physical processes through the integration and coordination of 3C (Computation, Communication, and Control) technology [3]-[5]. The integration of cyber networks and physical systems has significantly enhanced the efficiency and reliability of distribution system operations. However, the strong interdependence between cyber networks and physical power grids can bring more potential risks through complex communication links, which are significantly vulnerable to cyber security threats [6]. In addition, both the privatization of energy industries and the standardization of The associate editor coordinating the review of this article and approving it for publication was Mingjian Cui.

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“…There are several intrinsic applications in the Blockchain of Things (BCoT) [96]. Some of the potential applications include the following: a) smart grid [97], [98], b) healthcare [99], [100], c) Internet of Vehicles (IoV) [95], d) smart manufacturing [101], e) IoT [102], f) Industry 4.0 [103], g) smart transportation [104], and h) supply chain [105]. Fig.…”

Section: A Blockchain-based Authentication In Iovmentioning

confidence: 99%

Authentication Protocols in Internet of Vehicles: Taxonomy, Analysis, and Challenges

et al. 2020

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Internet of Vehicles (IoV) is treated as an extension of Vehicle-to-Vehicle (V2V) communication network. IoV helps in enhancing driving aids with the help of vehicle Artificial Intelligence (AI) awareness of other vehicles and their actions. IoV is connected in an adhoc networking environment which utilizes each vehicle in the network as a node, called Vehicular Ad Hoc Network (VANET), where the vehicles may be also connected to the public Internet. It is specifically important for the autonomous vehicles because they can instantaneously communicate with other vehicles surrounding them. In addition, safely avoiding accident prone zones is crucial in order to continue secure and smart transportation. Since the communication among various entities involved in the IoV environment is via open channel, it gives an opportunity to a passive/active adversary to intercept, modify, delete or even insert fake information during communication. It is then a serious concern for the vehicles users to determine whether the received information is genuine. In this survey paper, various security aspects, threats and attacks, network and threat models related to the IoV environment are discussed. Next, a taxonomy of security protocols is given that is essential to provide IoV data security. In particular, focus on various authentication protocols is given that is needed for mutual authentication among the involved entities in the IoV environment for secure communication. A detailed comparative analysis among various state-of-art authentication protocols proposed in the related IoV environment is provided to show their effectiveness as well as security and functionality features. Moreover, some testbeds are described that were designed and implemented for the IoV environment. In addition, some future challenges for IoV security protocols are also highlighted that are necessary to address in the future. INDEX TERMS Internet of Vehicles (IoV), vehicular adhoc networks (VANETs), authentication, batch verification, security.

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Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

Cited by 64 publications

References 67 publications

Cyber attacks in smart grid – dynamic impacts, analyses and recommendations

Cyber attacks in smart grid – dynamic impacts, analyses and recommendations

Multivariate Gaussian-Based False Data Detection Against Cyber-Attacks

Authentication Protocols in Internet of Vehicles: Taxonomy, Analysis, and Challenges

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