Key Management and Learning based Two Level Data Security for Metering Infrastructure of Smart Grid
This addresses security risks for smart grid systems, but it is incremental as it builds on existing key management schemes.
The paper tackles security vulnerabilities in smart grid metering infrastructure by proposing a two-level encryption method using partially trusted servers and a machine learning algorithm for node-to-node authentication, resulting in enhanced data privacy without increasing packet overhead or complexity.
In the smart grid, smart meters, and numerous control and monitoring applications employ bidirectional wireless communication, where security is a critical issue. In key management based encryption method for the smart grid, the Trusted Third Party (TTP), and links between the smart meter and the third party are assumed to be fully trusted and reliable. However, in wired/wireless medium, a man-in-middle may want to interfere, monitor and control the network, thus exposing its vulnerability. Acknowledging this, in this paper, we propose a novel two level encryption method based on two partially trusted simple servers (constitutes the TTP) which implement this method without increasing packet overhead. One server is responsible for data encryption between the meter and control center/central database, and the other server manages the random sequence of data transmission. Numerical calculation shows that the number of iterations required to decode a message is large which is quite impractical. Furthermore, we introduce One-class support vector machine (machine learning) algorithm for node-to-node authentication utilizing the location information and the data transmission history (node identity, packet size and frequency of transmission). This secures data communication privacy without increasing the complexity of the conventional key management scheme.