Gated Tri-Tower Transformer (GT3) - An Inflated Power Generation Attack Detector for Microgrids

Renewable energy microgrids are flourishing due to the rising environmental consciousness. Blockchain technology is considered a promising solution for advanced metering infrastructures (AMIs) connecting distributed microgrids, enabling power tracking and trading among participants. However, AMIs are vulnerable to attacks, particularly the Inflated Power Generation (IPG) attack, in which energy producers manipulate smart meters to report more energy than they actually produce. Currently, detecting IPG attacks relies mainly on human intervention, which is costly and time-consuming, making the process inefficient a nd n ot s calable. Although b lockchain-based AMIs possess desired features, they are not immune to IPG attacks since there is no on-chain ground truth to assess the reasonability of the reported amount of energy produced. This study proposes a novel Gated Tri-Tower Transformer (GT3) neural network architecture for a multivariate time series classifier t o serve as an IPG attack detector for each AMI blockchain node to validate energy transactions. The GT3 model captures temporal correlations and fuses different features to enhance the accuracy of IPG attack detection. Our experiments with the CAISO dataset demonstrate that the proposed detector outperforms the most advanced detectors, with an increased detection rate from 71.8% to 80.5% and a reduced false alarm rate from 12.2% to 5.6%. Moreover, we investigated and concluded that GT3 performs better than prior works even during the ramp-up periods for as little as one month of collected data.