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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2024 - 4

Enhancing V2G Network Security: A Novel Cockroach Behavior-Based Machine Learning Classifier to Mitigate MitM and DoS Attacks

MEKKAOUI, K. See more information about MEKKAOUI, K. on SCOPUS See more information about MEKKAOUI, K. on IEEExplore See more information about MEKKAOUI, K. on Web of Science
 
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Download PDF pdficon (896 KB) | Citation | Downloads: 194 | Views: 255

Author keywords
electric vehicles, smart grids, intrusion detection, supervised learning, communication networks

References keywords
detection(11), intrusion(10), vehicle(9), security(7), electronics(7), review(6), networks(6), network(6), learning(6), internet(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2024-05-31
Volume 24, Issue 2, Year 2024, On page(s): 31 - 40
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.02004
SCOPUS ID: 85195631972

Abstract
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V2G (Vehicle-to-Grid) is a system that allows an electric vehicle to connect and exchange energy with the electricity grid. This system is part of the smart-grid, which is an intelligent electricity network offering bidirectional communication and contributes to the environmental protection. Different actors are involved in communication in a V2G network, such as electric vehicles, charging stations, energy suppliers, and network operators, etc. Therefore, the V2G network faces several security challenges, such as data integrity, power system security, physical security of charging systems, data confidentiality and system interoperability. In this paper, an intrusion detection system (IDS) is proposed with the aim of predicting attacks in the V2G network. The study started with the generation of a dataset and the implementation of the Cockroach Behavior-Based Machine Learning Classifier with the objective of enhancing security of V2G networks by addressing Men-in-the-Middle (MitM) and Denial of Service (DoS) attacks. The simulation results, through the MiniV2G simulator, show that the proposed system achieved a detection accuracy of 98.93 %. This improves the reliability of the V2G network for users and better protects Electric Vehicle Charging Stations (EVCS) against DoS and MitM.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [SCOPUS Times Cited 3]


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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 5]


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[CrossRef] [SCOPUS Times Cited 3]


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[CrossRef] [SCOPUS Record]




References Weight

Web of Science® Citations for all references: 658 TCR
SCOPUS® Citations for all references: 1,251 TCR

Web of Science® Average Citations per reference: 23 ACR
SCOPUS® Average Citations per reference: 43 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-07-14 19:00 in 192 seconds.




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