  The widespread deployment of "smart" electric vehicle charging stations
(EVCSs) will be a key step toward achieving green transportation. The
connectivity features of smart EVCSs can be utilized to schedule EV charging
operations while respecting user preferences, thus avoiding synchronous
charging from a large number of customers and relieving grid congestion.
However, the communication and connectivity requirements involved in smart
charging raise cybersecurity concerns. In this work, we investigate charge
manipulation attacks (CMAs) against EV charging, in which an attacker
manipulates the information exchanged during smart charging operations. The
objective of CMAs is to shift the EV aggregator's demand across different times
of the day. The proposed CMAs can bypass existing protection mechanisms in EV
communication protocols. We quantify the impact of CMAs on the EV aggregator's
economic profit by modeling their participation in the day-ahead (DA) and
real-time (RT) electricity markets. Finally, we propose an unsupervised deep
learning-based mechanism to detect CMAs by monitoring the parameters involved
in EV charging. We extensively analyze the attack impact and the efficiency of
the proposed detection on real-world EV charging datasets. The results
highlight the vulnerabilities of smart charging operations and the need for a
monitoring mechanism to detect malicious CMAs.
