Deep Reinforcement Learning for Cybersecurity Assessment of Wind Integrated Power Systems

Liu, Xiaorui; Ospina, Juan; Konstantinou, Charalambos

doi:10.1109/access.2020.3038769

Cited by 45 publications

(18 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The security-enhanced and robust mechanisms -in the presence of motivated and sophisticated adversaries -should still be efficient for realistic implementation in IoT applications. Towards improving effectiveness, federated and reinforcement learning schemes can utilized in order for the the training to be performed on distributed data residing on intelligent electronic devices of the IoT network [118].…”

Section: Open Challengesmentioning

confidence: 99%

Consumer, Commercial, and Industrial IoT (In)Security: Attack Taxonomy and Case Studies

Xenofontos

Zografopoulos

Konstantinou

et al. 2022

IEEE Internet Things J.

Self Cite

View full text Add to dashboard Cite

Internet of Things (IoT) devices are becoming ubiquitous in our lives, with applications spanning from the consumer domain to commercial and industrial systems. The steep growth and vast adoption of IoT devices reinforce the importance of sound and robust cybersecurity practices during the device development life-cycles. IoT-related vulnerabilities, if successfully exploited can affect, not only the device itself, but also the application field in which the IoT device operates. Evidently, identifying and addressing every single vulnerability is an arduous, if not impossible, task. Attack taxonomies can assist in classifying attacks and their corresponding vulnerabilities. Security countermeasures and best practices can then be leveraged to mitigate threats and vulnerabilities before they emerge into catastrophic attacks and ensure overall secure IoT operation. Therefore, in this paper, we provide an attack taxonomy which takes into consideration the different layers of IoT stack, i.e., device, infrastructure, communication, and service, and each layer's designated characteristics which can be exploited by adversaries. Furthermore, using nine real-world cybersecurity incidents, that had targeted IoT devices deployed in the consumer, commercial, and industrial sectors, we describe the IoT-related vulnerabilities, exploitation procedures, attacks, impacts, and potential mitigation mechanisms and protection strategies. These (and many other) incidents highlight the underlying security concerns of IoT systems and demonstrate the potential attack impacts of such connected ecosystems, while the proposed taxonomy provides a systematic procedure to categorize attacks based on the affected layer and corresponding impact.

show abstract

Section: Open Challengesmentioning

confidence: 99%

Consumer, Commercial, and Industrial IoT (In)Security: Attack Taxonomy and Case Studies

Xenofontos

Zografopoulos

Konstantinou

et al. 2022

IEEE Internet Things J.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the power system is mainly governed by synchronous inertia, in order to ensure system stability, certain aspects of the electric grid need to be taken into consideration for RES integration (e.g., optimal location, power flow, generation variance, etc.) [22].…”

Section: Pillars Of a Secure And Resilient All-renewable Energy Gridmentioning

confidence: 99%

Toward a Secure and Resilient All-Renewable Energy Grid for Smart Cities

Konstantinou

2022

IEEE Consumer Electron. Mag.

Self Cite

View full text Add to dashboard Cite

The concept of smart cities is driven by the need to enhance citizens' quality of life. It is estimated that 70% of the world population will live in urban areas by 2050. The electric grid is the energy backbone of smart city deployments. An electric energy system immune to adverse events, both cyber and physical risks, and able to support the integration of renewable sources will drive a transformational development approach for future smart cities. This article describes how the future electric energy system with 100% electricity supply from renewable energy sources requires the "birth of security and resiliency" incorporated with its ecosystem.

show abstract

“…A deep Q-network (DQN) based approach has been used for accurate STLF [105]. In [106], a cyberphysical vulnerability assessment approach based DQN has been introduced, which requires sufficient power system data acquired to correctly identify the contingencies. From the previous studies, DRL models have some bottlenecks, such as the lack of compatibility with continuous action spaces and slow policy convergence.…”

Section: ) Deep Reinforcement Learningmentioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

View full text Add to dashboard Cite

The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems. INDEX TERMSSmart grid, deep learning, deep neural networks, edge computing, distributed and federated learning, power systems. NOMENCLATURE Abbreviations DDL Distributed deep learning DL Deep learning DRL Deep reinforcement learning DRN Deep residual network EI Edge intelligence EPS Electric power systems FL Federated learning IoT Internet of things LSTM Long short-term memory neural network The associate editor coordinating the review of this manuscript and approving it for publication was Shadi Alawneh . NN Neural network PVPF Photovoltaic power forecasting D. RESEARCH METHODOLOGY AND SYSTEMATIC REVIEW PROTOCOLStarting from September 2019, the multiple-methods approach was conducted [24]. The collection of the mainstream research papers on SG/AI from Web of Science (WoS), Scopus, IEEE Xplore, Science Direct, and Google scholar was conducted as the largest databases of peerreviewed articles. Only peer-reviewed articles written in English, providing experimental results, and having a unique identifier from the mentioned databases were taken into consideration, including reviews, research articles, patent reports, and conference proceedings. The adopted methodology for conducting this review article employs a combination of keywords categorized into three main groups, specifically, 'Deep Learning', 'Smart Grid', and 'Prediction'. The search methodology focuses on the recent research articles from 2015-2020 to identify the comprehensive statues of the AI applications on SG. The filtering process results in 220 research papers from 600 related papers selected based on their relevance by reading the title, abstract, conclusion,

show abstract

Deep Reinforcement Learning for Cybersecurity Assessment of Wind Integrated Power Systems

Cited by 45 publications

References 38 publications

Consumer, Commercial, and Industrial IoT (In)Security: Attack Taxonomy and Case Studies

Consumer, Commercial, and Industrial IoT (In)Security: Attack Taxonomy and Case Studies

Toward a Secure and Resilient All-Renewable Energy Grid for Smart Cities

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Contact Info

Product

Resources

About