Digital twin-driven SDN for smart grid: A deep learning integrated blockchain for cybersecurity.

Internet of Things (IoT)-enabled Smart Grid (SG) network is envisioned as the next-generation network for intelligent and efficient electric power transmission. In SG environment, the Smart Meters (SMs) mostly exchange services and data from Service Providers (SPs) via insecure public channel. This makes the entire SG ecosystem vulnerable to various security threats. Motivated from the aforementioned challenges, we incorporate Digital Twin (DT) technology, Software-Defined Networking (SDN), Deep Learning (DL) and blockchain into the design of a novel SG network. Specifically, a secure communication channel is first designed using an authentication method based on blockchain technology that has the ability to withstand a number of well-known assaults. Second, a new DL architecture that includes a self-attention mechanism, a Bidirectional-Gated Recurrent Unit (Bi-GRU) model, fully connected layers, and a softmax classifier is designed to enhance the attack detection process in SG environments. To deliver low latency and real-time services, the SDN is next employed as the network's backbone to send requests from SMs to a global SDN controller. DT technology is finally integrated into the SDN control plane, which stores the operating states and behavior models of SMs and communicates with SMs. The efficiency of the proposed framework is demonstrated by the blockchain implementation used in the SG network to assess computing time for the various numbers of transactions per block. Finally, the numerical results based on the N-BaIoT dataset shows better intrusion detection. • We incorporated a digital twin technology into a software-defined networking control plane, which stores and disseminates the operating states and behavior models of smart meters. • We used deep learning and blockchain in the design of smart grids to detect network intrusions. • A secure communication channel is designed using an authentication method based on blockchain technology that can withstand well-known attacks. • The proposed deep learning architecture uses a self-attention mechanism, a bidirectional-gated recurrent unit model, fully connected layers, and a softmax classifier, to detect intrusions. [ABSTRACT FROM AUTHOR]