| Summary: | The adoption of decentralized energy market models
facilitates the exchange of surplus power among local nodes in
peer-to-peer settings. However, decentralized energy transactions
within untrusted and non-transparent energy markets in modern
Smart Grids expose vulnerabilities and are susceptible to attacks.
One such attack is the False Data Injection Attack, where malicious entities intentionally inject misleading information into the
system. To address this threat, this paper proposes GridWatch,
an effective real-time in-network intelligent framework to detect
false data injection attacks. Gridwatch operates in a hybrid
model. It deploys inference model in the programmable network
devices and also on the server to detect false data injection
attacks. GridWatch was evaluated using a real-world dataset
from Austin, Texas, and can detect false data injection attacks
with 94.8% accuracy. GridWatch on average performs 4 billions
transactions per second in less than 1.8 microsecond latency.
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