Detection of Lying Electrical Vehicles in Charging Coordination Using Deep Learning

Abstract

Because charging coordination is a solution for avoiding grid instability by prioritizing charging requests, electric vehicles may lie and send false data to illegally receive higher charging priorities. In this paper, we first study the impact of such attacks on both the lying and honest electric vehicles. Our evaluations indicate that lying electric vehicles have a higher chance of charging, whereas honest electric vehicles may not be able to charge or may charge late. Then, an anomaly-based detector based on a deep neural network is devised to identify lying electric vehicles. The idea is that since each electric vehicle driver has a particular driving pattern, the data reported by the corresponding electric vehicle should follow this pattern, and any deviation due to reporting false data can be detected. To train the detector, we first create an honest dataset for the charging coordination application using real driving traces and information provided by an electric vehicle manufacturer, and we then propose a number of attacks as a basis for creating malicious data. We train and evaluate a gated recurrent unit model using this dataset. Our evaluations indicate that our detector can detect lying electric vehicles with high accuracy and a low false alarm rate even when tested on attacks that are not represented in the training dataset. INDEX TERMS Security, false data injection, charging coordination, electric vehicles, and smart grid. Nomenclature ADASY N Adaptive synthetic sampling AU C Area under the curve CC Charging coordinator DN N Deep neural network EV Electric vehicle GA Genetic algorithm GRU Gated recurrent unit M LP Multilayer perceptron N SGA Non-dominated Sorting Genetic Algorithm RN N Recurrent neural network ROC Receiver operating characteristic SM OT E Synthetic Minority Over-sampling Technique SoC State of charge T CC Time to complete charge

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