A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems
The rapid growth of electric vehicle (EV) penetration has led to more flexible and reliable vehicle-to-grid-enabled cyber-physical systems (V2G-CPSs). However, the increasing system complexity also makes them more vulnerable to cyber-physical threats. Coordinated cyber attacks (CCAs) have emerged as...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Online Access: | https://hdl.handle.net/10356/171272 |
| _version_ | 1826112864293748736 |
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| author | Ali, Mansoor Kaddoum, Georges Li, Wen-Tai Yuen, Chau Tariq, Muhammad Poor, H. Vincent |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ali, Mansoor Kaddoum, Georges Li, Wen-Tai Yuen, Chau Tariq, Muhammad Poor, H. Vincent |
| author_sort | Ali, Mansoor |
| collection | NTU |
| description | The rapid growth of electric vehicle (EV) penetration has led to more flexible and reliable vehicle-to-grid-enabled cyber-physical systems (V2G-CPSs). However, the increasing system complexity also makes them more vulnerable to cyber-physical threats. Coordinated cyber attacks (CCAs) have emerged as a major concern, requiring effective detection and mitigation strategies within V2G-CPSs. Digital twin (DT) technologies have shown promise in mitigating system complexity and providing diverse functionalities for complex tasks such as system monitoring, analysis, and optimal control. This paper presents a resilient and secure framework for CCA detection and mitigation in V2G-CPSs, leveraging a smart DT-enabled approach. The framework introduces a smarter DT orchestrator that utilizes long short-term memory (LSTM) based actor-critic deep reinforcement learning (LSTM-DRL) in the DT virtual replica. The LSTM algorithm estimates the system states, which are then used by the DRL network to detect CCAs and take appropriate actions to minimize their impact. To validate the effectiveness and practicality of the proposed smart DT framework, case studies are conducted on an IEEE 30 bus system-based V2G-CPS, considering different CCA types such as malicious V2G node or control command attacks. The results demonstrate that the framework is capable of accurately estimating system states, detecting various CCAs, and mitigating the impact of attacks within 5 seconds. |
| first_indexed | 2024-10-01T03:14:05Z |
| format | Journal Article |
| id | ntu-10356/171272 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:14:05Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1712722023-10-18T03:18:00Z A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems Ali, Mansoor Kaddoum, Georges Li, Wen-Tai Yuen, Chau Tariq, Muhammad Poor, H. Vincent School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Vehicle-to-grid Cyberattack The rapid growth of electric vehicle (EV) penetration has led to more flexible and reliable vehicle-to-grid-enabled cyber-physical systems (V2G-CPSs). However, the increasing system complexity also makes them more vulnerable to cyber-physical threats. Coordinated cyber attacks (CCAs) have emerged as a major concern, requiring effective detection and mitigation strategies within V2G-CPSs. Digital twin (DT) technologies have shown promise in mitigating system complexity and providing diverse functionalities for complex tasks such as system monitoring, analysis, and optimal control. This paper presents a resilient and secure framework for CCA detection and mitigation in V2G-CPSs, leveraging a smart DT-enabled approach. The framework introduces a smarter DT orchestrator that utilizes long short-term memory (LSTM) based actor-critic deep reinforcement learning (LSTM-DRL) in the DT virtual replica. The LSTM algorithm estimates the system states, which are then used by the DRL network to detect CCAs and take appropriate actions to minimize their impact. To validate the effectiveness and practicality of the proposed smart DT framework, case studies are conducted on an IEEE 30 bus system-based V2G-CPS, considering different CCA types such as malicious V2G node or control command attacks. The results demonstrate that the framework is capable of accurately estimating system states, detecting various CCAs, and mitigating the impact of attacks within 5 seconds. The work of H. Vincent Poor was supported by the U.S. National Science Foundation under Grant CNS-2128448 and Grant ECCS-2335876. 2023-10-18T03:18:00Z 2023-10-18T03:18:00Z 2023 Journal Article Ali, M., Kaddoum, G., Li, W., Yuen, C., Tariq, M. & Poor, H. V. (2023). A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems. IEEE Transactions On Information Forensics and Security, 18, 5258-5271. https://dx.doi.org/10.1109/TIFS.2023.3305916 1556-6013 https://hdl.handle.net/10356/171272 10.1109/TIFS.2023.3305916 2-s2.0-85168750633 18 5258 5271 en IEEE Transactions on Information Forensics and Security © 2023 IEEE. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Vehicle-to-grid Cyberattack Ali, Mansoor Kaddoum, Georges Li, Wen-Tai Yuen, Chau Tariq, Muhammad Poor, H. Vincent A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems |
| title | A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems |
| title_full | A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems |
| title_fullStr | A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems |
| title_full_unstemmed | A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems |
| title_short | A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems |
| title_sort | smart digital twin enabled security framework for vehicle to grid cyber physical systems |
| topic | Engineering::Electrical and electronic engineering Vehicle-to-grid Cyberattack |
| url | https://hdl.handle.net/10356/171272 |
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