TY - JOUR
T1 - AI-enhanced smart grid framework for intrusion detection and mitigation in EV charging stations
AU - Singh, Arvind R.
AU - Kumar, R. Seshu
AU - Rathore, Rajkumar Singh
AU - Pandian, A.
AU - Alrayes, Fatma S.
AU - Allafi, Randa
AU - Ahmad, Nazir
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/12/26
Y1 - 2024/12/26
N2 - The rapid growth of electric vehicle (EV) charging stations has brought about substantial security and reliability challenges within smart grid systems. As EV adoption continues to rise, the importance of building a secure and resilient charging infrastructure becomes increasingly evident, especially with the integration of renewable energy sources and decentralized grid operations. This paper introduces an Artificial Intelligence-powered Smart Grid Framework (AI-SGF), developed to tackle intrusion detection and mitigation specifically in EV charging stations. Existing studies have highlighted vulnerabilities in these stations, exposing the smart grid to risks such as unauthorized access, data tampering, and cyber-physical attacks. The AI-SGF addresses these concerns by continuously monitoring real-time charging data, allowing it to detect anomalies and swiftly counter potential cyber threats. The framework has undergone extensive testing, proving its effectiveness with precision, recall, and F1 scores of 96.8 %, 96.0 %, and 96.4 % for reliability, and a cyberattack detection score of 98.9 %. These metrics affirm AI-SGF's capability to exceed current benchmarks in cybersecurity for EV infrastructure, providing robust protection and integration across energy distribution networks. The scalable and adaptable design of AI-SGF further supports seamless deployment within existing EV charging networks, promoting a secure and sustainable EV ecosystem.
AB - The rapid growth of electric vehicle (EV) charging stations has brought about substantial security and reliability challenges within smart grid systems. As EV adoption continues to rise, the importance of building a secure and resilient charging infrastructure becomes increasingly evident, especially with the integration of renewable energy sources and decentralized grid operations. This paper introduces an Artificial Intelligence-powered Smart Grid Framework (AI-SGF), developed to tackle intrusion detection and mitigation specifically in EV charging stations. Existing studies have highlighted vulnerabilities in these stations, exposing the smart grid to risks such as unauthorized access, data tampering, and cyber-physical attacks. The AI-SGF addresses these concerns by continuously monitoring real-time charging data, allowing it to detect anomalies and swiftly counter potential cyber threats. The framework has undergone extensive testing, proving its effectiveness with precision, recall, and F1 scores of 96.8 %, 96.0 %, and 96.4 % for reliability, and a cyberattack detection score of 98.9 %. These metrics affirm AI-SGF's capability to exceed current benchmarks in cybersecurity for EV infrastructure, providing robust protection and integration across energy distribution networks. The scalable and adaptable design of AI-SGF further supports seamless deployment within existing EV charging networks, promoting a secure and sustainable EV ecosystem.
KW - Artificial Intelligence (AI)
KW - Cyber-physical attacks
KW - Electric vehicle
KW - Machine Learning (ML)
KW - Smart grid
UR - http://www.scopus.com/inward/record.url?scp=85214210222&partnerID=8YFLogxK
U2 - 10.1016/j.aej.2024.12.061
DO - 10.1016/j.aej.2024.12.061
M3 - Article
AN - SCOPUS:85214210222
SN - 1110-0168
JO - Alexandria Engineering Journal
JF - Alexandria Engineering Journal
ER -