  In this paper, we investigate an edge-based approach for the detection and
localization of coordinated oscillatory load attacks initiated by exploited EV
charging stations against the power grid. We rely on the behavioral
characteristics of the power grid in the presence of interconnected EVCS while
combining cyber and physical layer features to implement deep learning
algorithms for the effective detection of oscillatory load attacks at the EVCS.
We evaluate the proposed detection approach by building a real-time test bed to
synthesize benign and malicious data, which was generated by analyzing
real-life EV charging data collected during recent years. The results
demonstrate the effectiveness of the implemented approach with the
Convolutional Long-Short Term Memory model producing optimal classification
accuracy (99.4\%). Moreover, our analysis results shed light on the impact of
such detection mechanisms towards building resiliency into different levels of
the EV charging ecosystem while allowing power grid operators to localize
attacks and take further mitigation measures. Specifically, we managed to
decentralize the detection mechanism of oscillatory load attacks and create an
effective alternative for operator-centric mechanisms to mitigate
multi-operator and MitM oscillatory load attacks against the power grid.
Finally, we leverage the created test bed to evaluate a distributed mitigation
technique, which can be deployed on public/private charging stations to average
out the impact of oscillatory load attacks while allowing the power system to
recover smoothly within 1 second with minimal overhead.
