Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services. (arXiv:2208.05073v1 [cs.CR])

In this paper, we study the expanding attack surface of Adversarial Machine
Learning (AML) and the potential attacks against Vehicle-to-Microgrid (V2M)
services. We present an anticipatory study of a multi-stage gray-box attack
that can achieve a comparable result to a white-box attack. Adversaries aim to
deceive the targeted Machine Learning (ML) classifier at the network edge to
misclassify the incoming energy requests from microgrids. With an inference
attack, an adversary can collect real-time data from the communication between
smart microgrids …

adversarial machine machine learning services threats vehicle