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In smart grid, malicious customers may compromise their smart meters (SMs) toreport false readings to achieve financial gains illegally. Reporting falsereadings not only causes hefty financial losses to the utility but may alsodegrade the grid performance because the reported readings are used for energymanagement. This paper is the first work that investigates this problem in thenet-metering system, in which one SM is used to report the difference betweenthe power consumed and the power generated. First, we prepare a benign datasetfor the net-metering system by processing a real power consumption andgeneration dataset. Then, we propose a new set of attacks tailored for thenet-metering system to create malicious dataset. After that, we analyze thedata and we found time correlations between the net meter readings andcorrelations between the readings and relevant data obtained from trustworthysources such as the solar irradiance and temperature. Based on the dataanalysis, we propose a general multi-data-source deep hybrid learning-baseddetector to identify the false-reading attacks. Our detector is trained on netmeter readings of all customers besides data from the trustworthy sources toenhance the detector performance by learning the correlations between them. Therationale here is that although an attacker can report false readings, hecannot manipulate the solar irradiance and temperature values because they arebeyond his control. Extensive experiments have been conducted, and the resultsindicate that our detector can identify the false-reading attacks with highdetection rate and low false alarm.

Detection of False-Reading Attacks in the AMI Net-Metering System