Data centers in the smart grid, which automate the business of electricity delivery through the integration of the electronic technology and the electrical infrastructure Because the smart grid relies on the transfer of sensitive data, keeping that data secure from unauthorized access and other cyber threats is extremely difficult. In particular, the issued system focuses on a new method, which exploring the CNN with RNN using LSTM networks to a smart grid security and communication protocol. Realizing this, the hybrid CNN, RNN, and LSTM models proposed in this manuscript hierarchically focus on multi-level domains for data privacy and security. Your CNN portion is great for grabbing the spatial features of the data being passed to it, which itself can help to get a read on whether the information being relayed is suspicious or not. On the other hand, the RNN part of the model is trained to recognize temporal dependencies within the data, allowing it to provide a system capable of recognizing sequences of actions[6] that may indicate potential threats or breaches. Moreover, LSTM module's ability to retain recent information helps it to learns from past data effectively before making predictions on future patterns, empowering the system to anticipate and address potential safety threats proactively. By using the hybrid of CNN, RNN, and LSTM, the smart grid can learn and adapt over time, making it more effective at detecting and preventing attacks, which in turn reduces the likelihood of data breaches and protects the privacy of data for both consumers and service providers. In addition, it is to mention that the secure message channels integration with the DNN-based protection system guarantees the security of those sensitive data while providing end-to-end encryption for the users.
Authors: V.Anusha Sowbarnika, S. Ramya, D. Praveena, Lydia D. Issac, S. B. G. Tilak Babu, Mohit Tiwari
DOI: https://doi.org/10.2139/ssrn.5075191
Publish Year: 2025
