原文信息：
False data injection attacks on data markets for electric vehicle charging stations
原文链接：
https://www.sciencedirect.com/science/article/pii/S2666792422000166
Abstract
Modern societies use machine learning techniques to support complex decision-making processes (e.g., renewable energy and power demand forecasting in energy systems). Data fuels these techniques, so the quality of the data fed into them determines the accuracy of the results. While the amount of data is increasing with the adoption of internet-of-things, most of it is still private. Availability of data limits the application of machine learning. Scientists and industry pioneers are proposing a model that relies on the economics of data markets, where private data can be traded for a price. Cybersecurity analyses of such markets are lacking. In this context, our study makes two contributions. First, it designs a data market for electric vehicle charging stations, which aims to improve the accuracy of electric vehicle charging demand forecasts. Accurate demand forecasts are essential for sustainable operations of the electric vehicle - charging station - power grid ecosystem, which, in turn, facilitates the electrification and decarbonization of the transportation sector. On the other hand, erroneous demand forecasts caused by malicious cyberattacks impose operational challenges to the ecosystem. Thus, the second contribution of our study is to examine the feasibility of false data injection attacks on the data market for electric vehicle charging stations and to propose a defense mechanism against such attacks. We illustrate our results using data from electric vehicle charging stations in Manhattan, New York. We demonstrate that the data market improves forecasting accuracy of charging stations and reduces the effectiveness of false data injection attacks. The purpose of this work is not only to inform electric vehicle charging stations about the economic benefits of data markets, but to promote cyber awareness among data market pioneers and stakeholders.
Keywords
Data markets
Demand forecasts
Electric vehicle charging stations
Kullback-Leibler divergence
Machine learning
Quantile linear regression
Graphics
Fig. 1. Operation modes (1–6) of EVCS data market and compromised data owners under a false data injection cyberattack on the data market.
Fig. 2. Computations of the data market operator.
Fig. 3. (a) Training and (b) Test errors, based on the Pinball loss in Eq. (4b), of the EVCS demand forecast models. The models have 𝛼 = 0.5.
Fig. 4. (a) Difffference in EVCS demand forecasts using public+private data 𝛼 =0.5, (b) EVCS demand forecasts using public+private data 𝛼 = [0.1, 0.5, 0.9].
Fig. 5. Shapley values and the data payments (in cents) of the data features used for forecasting the EVCS demand in Fig 4(a). The data payments are scaled down by 1000 to visualize them and Shapley values together.
Fig. 6. False data injection attack on the data market to reduce the EVCS demand forecast by 10% at 𝑡 = 13, launched by compromising EV 𝑆𝑜𝐶 data.
Fig. 7. False data injection attack on the data market to reduce the EVCS demand forecast by 10% at 𝑡 =13, by compromising data of neighborhood EVCSs.
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