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Deep Neural Networks have proven to be highly accurate at a variety of tasksin recent years. The benefits of Deep Neural Networks have also been embracedin power grids to detect False Data Injection Attacks (FDIA) while conductingcritical tasks like state estimation. However, the vulnerabilities of DNNsalong with the distinct infrastructure of cyber-physical-system (CPS) can favorthe attackers to bypass the detection mechanism. Moreover, the divergent natureof CPS engenders limitations to the conventional defense mechanisms for FalseData Injection Attacks. In this paper, we propose a DNN framework withadditional layer which utilizes randomization to mitigate the adversarialeffect by padding the inputs. The primary advantage of our method is whendeployed to a DNN model it has trivial impact on the models performance evenwith larger padding sizes. We demonstrate the favorable outcome of theframework through simulation using the IEEE 14-bus, 30-bus, 118-bus and 300-bussystems. Furthermore to justify the framework we select attack techniques thatgenerate subtle adversarial examples that can bypass the detection mechanismeffortlessly.

Mitigating Adversarial Effects of False Data Injection Attacks in Power Grid