  The electric grid is an attractive target for cyberattackers given its
critical nature in society. With the increasing sophistication of cyberattacks,
effective grid defense will benefit from proactively identifying
vulnerabilities and attack strategies. We develop a deep reinforcement
learning-based method that recognizes vulnerabilities in load frequency
control, an essential process that maintains grid security and reliability. We
demonstrate how our method can synthesize a variety of attacks involving false
data injection and load switching, while specifying the attack and threat
models - providing insight into potential attack strategies and impact. We
discuss how our approach can be employed for testing electric grid
vulnerabilities. Moreover our method can be employed to generate data to inform
the design of defense strategies and develop attack detection methods. For
this, we design and compare a (deep learning-based) supervised attack detector
with an unsupervised anomaly detector to highlight the benefits of developing
defense strategies based on identified attack strategies.
