An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-Based Systems

Khan, Asad Ali; Beg, Omar Ali; Jin, Yufang; Ahmed, Sara

doi:10.1109/access.2023.3289887

Cited by 2 publications

(7 citation statements)

References 45 publications

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“…One solution to mitigate such cyber-attacks and maintain the stable operation of microgrids is to develop a resilient controller [8,27,95,[99][100][101][102][103][104]. AI-based techniques are being utilized to design resilient control schemes in microgrids to mitigate the malicious effects of such attacks [42][43][44][45][46]105]. Because of its low computing overhead, effectiveness, and simplicity in design and implementation in a distributed control system, adaptive neuro-fuzzy infer-ence systems (ANFISs) are used for cyber-attack mitigation in an islanded DC microgrid in [42,43].…”

Section: Ai-based Cyber-attack Mitigationmentioning

confidence: 99%

“…NARX ANN is a special class of recurrent neural networks best suited for time series data prediction, input-output modeling of nonlinear dynamical systems, and cyber attack detection in microgrids. Therefore, NARX ANN-based resilient controller is designed to mitigate the cyber-attacks in distributed cooperative control-based AC and DC microgrids in [44,106], respectively.…”

Section: Ai-based Cyber-attack Mitigationmentioning

confidence: 99%

“…The proposed controller is trained using the data obtained by simulating the test microgrid system under varying operating conditions. After optimal selection of NARX ANN parameters during offline training, it is deployed as an estimator to generate the reference for the proportional-integral-based controller in [106] whereas, it acts as a secondary level controller to replace the conventional PI-based controller in [44]. Feed-forward ANNs are used to make the existing control resilient in both AC and DC microgrids and showed the improved performance to mitigate the cyber-attacks [46,57,105,[107][108][109].…”

Section: Ai-based Cyber-attack Mitigationmentioning

confidence: 99%

“…AI-based resilient control architectures can improve the reliability of the power network. The learning capabilities of artificial neural networks can mitigate the effects of cyber-attacks [42][43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Beg,

Khan,

Rehman

et al. 2023

Energies

Self Cite

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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: Ai-based Cyber-attack Mitigationmentioning

confidence: 99%

Section: Ai-based Cyber-attack Mitigationmentioning

confidence: 99%

Section: Ai-based Cyber-attack Mitigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Beg,

Khan,

Rehman

et al. 2023

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Existing mitigation systems employed in VPPs, such as the proportional-integral (PI)based mitigation system [11], exhibit sluggish response times, necessitate a heightened accuracy, and cause voltage deviation. PI controllers are known for their straightforward structure, incorporating proportional and integral components that facilitate efficient system regulation.…”

Section: Introductionmentioning

confidence: 99%

Mitigating Cyber Anomalies in Virtual Power Plants Using Artificial-Neural-Network-Based Secondary Control with a Federated Learning-Trust Adaptation

Taheri,

Davoodi,

Ali

2024

Energies

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Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive communication layer. FL-trust, an improved federated learning (FL) approach, has been recently introduced as a mitigation system for cyber-attacks. However, current FL-trust enhancements, relying solely on proportional-integral (PI), exhibit drawbacks like sensitivity to controller gain fluctuations and a slow response to sudden disturbances, and conventional FL-trust is not directly applicable to the non-independent and identically distributed (non-IID) datasets common in VPPs. To address these limitations, we introduce an artificial neural network (ANN)-based technique to adapt FL-trust to non-IID datasets. The ANN is designed as an intelligent anomaly mitigation control method, employing a dynamic recurrent neural network with exogenous inputs. We consider the effects of the most common VPP attacks, poisoning attacks, on the distributed cooperative controller at the secondary control level. The ANN is trained offline and tested online in the simulated VPP. Using MATLAB simulations on a HOMER-modeled VPP, the proposed technique demonstrates its superior ability to sustain normal VPP operation amidst cyber anomalies, outperforming a PI-based mitigation system in accuracy and detection speed.

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An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-Based Systems

Cited by 2 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

Mitigating Cyber Anomalies in Virtual Power Plants Using Artificial-Neural-Network-Based Secondary Control with a Federated Learning-Trust Adaptation

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