Energy Theft Detection with Energy Privacy Preservation in the Smart Grid
As a prominent early instance of the IoT in the smart grid,the advanced metering infrastructure(AMI)provides real-time information from smart meters to both grid operators and customers,exploiting the full potential of demand response.However,the newly-collected information without security protection can be maliciously altered and result in huge loss.In this paper,we propose an energy theft detection scheme with energy privacy preservation in the smart grid.Specially,we use combined convolutional neural networks(CNN)to detect abnormal behavior of the metering data from a long-period pattern observation.In addition,we employ Paillier algorithm to protect the energy privacy.In other words,the users' energy data are securely protected in the transmission and the data disclosure is minimized.Our security analysis demonstrates that in our scheme data privacy and authentication are both achieved.Experimental results illustrate that our modified CNN model can effectively detect abnormal behaviors at an accuracy up to 92.67%.
Energy Theft Privacy Preserving CNN Smart Grid
Donghuan Yao Mi Wen Xiaohui Liang Zipeng Fu Kai Zhang Baojia Yang
College of Computer Science and Technology,Shanghai University of Electric Power,Shanghai 201101,Chi Department of Computer Science,University of Massachusetts Boston Department of Computer Science,University of California,Los Angeles Los Angeles,CA 90095,USA Microsoft Suzhou(Office)
国内会议
上海
英文
84-95
2019-05-17（万方平台首次上网日期，不代表论文的发表时间）