  Deep Neural Networks have proven to be highly accurate at a variety of tasks
in recent years. The benefits of Deep Neural Networks have also been embraced
in power grids to detect False Data Injection Attacks (FDIA) while conducting
critical tasks like state estimation. However, the vulnerabilities of DNNs
along with the distinct infrastructure of cyber-physical-system (CPS) can favor
the attackers to bypass the detection mechanism. Moreover, the divergent nature
of CPS engenders limitations to the conventional defense mechanisms for False
Data Injection Attacks. In this paper, we propose a DNN framework with
additional layer which utilizes randomization to mitigate the adversarial
effect by padding the inputs. The primary advantage of our method is when
deployed to a DNN model it has trivial impact on the models performance even
with larger padding sizes. We demonstrate the favorable outcome of the
framework through simulation using the IEEE 14-bus, 30-bus, 118-bus and 300-bus
systems. Furthermore to justify the framework we select attack techniques that
generate subtle adversarial examples that can bypass the detection mechanism
effortlessly.
