Cyber Vulnerabilities of Modern Power Systems

Khan, Asad Ali; Beg, Omar Ali

doi:10.1007/978-3-031-20360-2_2

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…To carry out these kinds of attacks, one is not required to be familiar with the microgrids' settings or have the necessary skills to alter control messages and measurements. During the DoS cyber-attack on the Ukrainian power grid, operators were unable to communicate control signals to the actuators [10]. Wide area networks can be vulnerable to DoS cyber-attacks if malware is placed in substation routers, giving attackers access to phasor measurement unit (PMU) communications.…”

Section: Denial-of-service Cyber-attackmentioning

confidence: 99%

“…Due to a cyber-attack brought on by malware known as BlackEnergy in control center computers, Ukraine's power infrastructure failed in December 2015, knocking out thousands of homes and facilities. A significant percentage of consumers would lose power due to such malfunctions and cyber-attacks, and very sensitive and mission-critical equipment may suffer serious harm [10][11][12][13][14][15]. Table 1 summarizes the actual reported cyber-attacks on the energy industry [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

Beg,

Khan,

Rehman

et al. 2023

Energies

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In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.

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Section: Denial-of-service Cyber-attackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

Beg,

Khan,

Rehman

et al. 2023

Energies

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“…Information is gained by the attacker impersonating both the server and client, hence the threat is that the attacker is able to capture, receive and sent information that is intended for another person on the network [34], [35]. In this attack, real time operations of information exchange, transactional operations and communication are interfered with [36]. Different types of attacks in this case are spoofing of IP and hijacking of sessions on the network [37], [38], [39].…”

Section: Man-in-the-middle (Mitm) Attacksmentioning

confidence: 99%

Assessment of existing cyber-attack detection models for web-based systems

Awuor¹

2023

Global J. Eng. Technol. Adv.

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In the current technological environment, different entities engage in intricate cyber security approaches in order to counter damages and disruptions in web-based systems. The design of the security protocols relies on the guarantee that attacks are prevented in the web-based systems. Prevention and detection using techniques such as access control tools, encryption and firewalls present limitations in the full protection of web-based systems. Furthermore, despite the sophistication of current systems, there are still shortfalls in high false positive and false negative threat detection rates, which is attributed to poor adaptation by systems and networks to the changing threats and behavior of cyber-criminals. In this perspective, this survey paper discusses the existing cyber-attack detection models, and recommends the cyber-attack detection models and techniques that are appropriate for web-based systems. It is evident that deep learning techniques offer better performance and robustness compared to traditional machine learning techniques and other non-artificial intelligence-based techniques. Deep learning techniques learn and extract features automatically without human intervention and can also handle big and multidimensional data more conventionally than the other techniques.

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“…The vulnerabilities of cyberphysical network (CPS) based control systems in DC microgrids are multifaceted [31]. First, the interconnected nature of DERs and their reliance on communication networks exposes them to potential unauthorized access and control [32]. Malicious actors could exploit vulnerabilities in communication protocols or gain unauthorized access to the control infrastructure [33].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Replay Attack Impact in DC Microgrid Consensus Control: Detection and Mitigation by Kalman-Filter-Based Observer

Taher,

Tariq,

Behnamfar

et al. 2023

IEEE Access

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This paper adopts a Direct Current (DC) Microgrid with a consensus-based secondary control strategy to efficiently manage the microgrid, ensuring precise voltage regulation and fair power distribution among Distributed Energy Resources (DERs). The consensus approach employs a communication network to establish a Cyber layer for DER information exchange and harmonize the entire system to achieve a common goal. However, this cyber layer's vulnerability could destabilize the interconnected control system and lead to the whole system being unsynchronized. The paper explores replay attacks conducted in the path of the communication link between interconnected Distributed Generation Units (DGUs), demonstrating their impact on secondary control using simulations and real-time experiments. A method leveraging Distributed Kalman Filter observers for attack detection is proposed which requires low computational demand to suit the real-time application, while an attack mitigation technique using observer-generated residues with the optimal condition is implemented. Validation is conducted through both MATLAB simulations and realtime OPAL-RT experiments, confirming the proposed system's effectiveness.

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Cyber Vulnerabilities of Modern Power Systems

Cited by 6 publications

References 45 publications

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

Assessment of existing cyber-attack detection models for web-based systems

Analyzing Replay Attack Impact in DC Microgrid Consensus Control: Detection and Mitigation by Kalman-Filter-Based Observer

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