A Secure and Anonymous Communication Scheme for Charging Information in Vehicle-to-Grid

Luo, Jingtang; Yao, Shiying; Zhang, Jiamin; Xu, Wei; Yun, He; Zhang, Min

doi:10.1109/access.2020.3005400

Cited by 11 publications

(2 citation statements)

References 34 publications

(37 reference statements)

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“…This information poses a threat to privacy and could be exploited for criminal purposes such as burglary. To address this issue, various privacy-preserving [1], [9], [52], secure communication solutions [53], [54], and secure federated learning approaches [55] have been proposed in the literature. However, our focus in this paper is solely on using RL techniques for electricity theft detection, and we do not address privacy and secure communication concerns.…”

Section: ) Data-driven Methodsmentioning

confidence: 99%

Electricity Theft Detection Using Deep Reinforcement Learning in Smart Power Grids

et al. 2023

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In smart power grids, smart meters (SMs) are deployed at the end side of customers to report fine-grained power consumption readings periodically to the utility for energy management and load monitoring. However, electricity theft cyber-attacks can be launched by fraudulent customers through compromising their SMs to report false readings to pay less for their electricity usage. These attacks harmfully affect the power sector since they cause substantial financial loss and degrade the grid performance because the readings are used for energy management. Supervised machine learning approaches have been used in the literature to detect the attacks, but to the best of our knowledge, the use of reinforcement learning (RL) has not been investigated yet. RL can be better than the existing approaches because it can adapt more efficiently with the dynamic nature of cyber-attacks and consumption patterns due to its capability to learn by exploration and exploitation mechanisms and deciding optimal actions. In this article, a deep reinforcement learning (DRL) approach is proposed as a promising solution to the electricity theft problem. The samples of real dataset are employed as an environment and rewards are given based on detection errors made during training. In particular, the proposed approach is presented in four different scenarios. First, a global detection model is constructed using a deep Q network (DQN) and a double deep Q network (DDQN) with different architectures of deep neural networks. Second, the global detector is used to build a customized detection model for new customers to achieve high detection accuracy while preventing zeroday attacks. Third, changing the consumption pattern of the existing customers is taken into consideration in the third scenario. Fourth, the challenges of defending against newly launched cyber-attacks are addressed in the fourth scenario. Extensive experiments have been conducted, and the results demonstrate that the proposed DRL approach can boost the detection of electricity theft cyberattacks, and it can efficiently learn new consumption patterns, changes in the consumption patterns of existing customers, and newly launched cyber-attacks.INDEX TERMS Security, electricity theft, false reading attacks, reinforcement learning, zero-day attacks, smart power grids.The associate editor coordinating the review of this manuscript and approving it for publication was Neetesh Saxena .

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Section: ) Data-driven Methodsmentioning

confidence: 99%

Electricity Theft Detection Using Deep Reinforcement Learning in Smart Power Grids

et al. 2023

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“…To settle the authentication, communication message confidentiality, and identity privacy secrecy during the communication of EVs in V2G networks, a trusted computing-based solution for secure V2G communication is proposed in [148,149]. Authentication techniques, which were introduced to key distributions without trusted third parties in [150], improve the security of public network communication by decomposition and random forwarding. Based on software-defined networking (SDN) technology, a communication network architecture is established between the data layer and the control layer, and EVs for V2G applications [151].…”

Section: V2gmentioning

confidence: 99%

Vehicle to everything in the power grid (V2eG): A review on the participation of electric vehicles in power grid economic dispatch

Song

Chen

Yang

et al. 2022

Energy Conversion and Econom

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The increasing popularity of electric vehicles (EVs) and the enhanced energy storage capability of batteries have made EVs adjustable resources in economic dispatching for power grids. The guidance and control of discharging EVs have become issues with everincreasing concerns, and the EVs can be discharged to other entities in the grid, which is called vehicle to everything in the power grid (V2eG) technology. When V2eG technologies become widespread, EVs could be widely distributed, fully controllable, and marketable power sources. As ideal mobile power sources, EVs are geographically available all the time. In this paper, the application process of V2eG technology is first introduced, and the current research status of V2eG technology is discussed. The modelling technology involved in V2eG technology is next analysed and discussed in detail. Then, the applications of V2eG technology in economic dispatch are summarized. Furthermore, research on communication technology for V2eG technology is investigated. In addition, the policies and electricity market access criteria related to V2eG are discussed, and the corresponding trading mechanisms are analysed. Finally, the related V2eG technologies are summarized, and the future development direction of V2eG technology is prospected.

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Enhancing Anonymity of Internet of Vehicle Identities in Connected Vehicle Security Services Using Batch Verification Algorithm

Dwivedi,

Agarwal,

Shukla

2024

Lecture Notes in Networks and Systems

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A Secure and Anonymous Communication Scheme for Charging Information in Vehicle-to-Grid

Cited by 11 publications

References 34 publications

Electricity Theft Detection Using Deep Reinforcement Learning in Smart Power Grids

Electricity Theft Detection Using Deep Reinforcement Learning in Smart Power Grids

Vehicle to everything in the power grid (V2eG): A review on the participation of electric vehicles in power grid economic dispatch

Enhancing Anonymity of Internet of Vehicle Identities in Connected Vehicle Security Services Using Batch Verification Algorithm

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