DESIGN AND DEVELOPMENT OF SECURITY FRAMEWORK FOR SMART GRID INFRASTRUCTURE

Abstract

The smart grid is considered the future of electricity networks because it brings in intelligence, flexibility, and reliability to the power system. Unlike traditional power grids, smart grids use advanced information and communication technologies to enable two-way communication between utility companies and consumers. This makes it possible to integrate renewable energy sources, improve energy efficiency, and ensure better management of resources. However, as the smart grid becomes more connected and dependent on digital systems, it faces serious challenges related to cybersecurity. The openness and interconnectedness that make it efficient also make it vulnerable to different types of cyberattacks, data breaches, and unautho- rized access. If these issues are not addressed properly, they can affect the privacy of consumers, disrupt services, or even cause large-scale blackouts. This thesis presents an advanced, multi-layered framework for secure smart grid in- frastructure, integrating the strengths of deep learning, blockchain technology, and immersive collaborative platforms. Addressing the critical gaps in cybersecurity, privacy, and operational scalability, the research rethinks smart grid protection by unifying decentralized trust, intelligent anomaly detection, and efficient large-scale data management. The first major contribution is a robust secure data sharing architecture combin- ing a hybrid deep learning intrusion detection system using Variational Autoen- iv v coder (VAE) and Attention-based Bidirectional LSTM (ABiLSTM) with blockchain- backed audit trails and off-chain Inter Planetary File System (IPFS) storage. Ex- perimental validation on benchmark ToN-IoT and BoT-IoT datasets demonstrates significant performance improvements. The proposed system achieves near-perfect detection in percentage, accuracy (99.99), precision (98.99), recall (99.9), and F1 score (99.91), outperforming classical approaches (Naive Bayes, Decision Tree, Ran- dom Forest) by wide margins. Importantly, the hybrid on/off-chain design substan- tially reduces blockchain overhead, enabling real-time scalability lacking in earlier ledger-centric models. The research extends to IoT-enabled Electric Vehicles (EVs), devising a secure and privacy-preserving framework that leverages Stacked Sparse Denoising Autoencoders (SSDAE) and Attention-based LSTM for anomaly detection and data anonymiza- tion. Coupled with smart contract-driven authentication, this approach achieves highly effective multi-class threat detection and privacy protection, with detection and recall rates surpassing leading prior Long Short Term Memory (LSTM) and Artificial Neural Network (ANN) models while ensuring low-latency communication essential for mobile EV-grid integration. A novel facet of this thesis is the application of metaverse and digital twin technolo- gies, which enable unprecedented real-time, immersive collaboration and situational awareness for grid operators. Through federated and collaborative intrusion detec- tion, multi-operator security, and grid incident response now occur with reduced detection latency and enhanced visibility, an improvement over the isolated or man- ual supervision that dominated earlier solutions. In summary, this thesis offers (1) higher detection accuracy and reliability through advanced deep learning architectures; (2) integrated, scalable security and privacy vi solutions across grid and IoT-vehicle domains; (3) immersive, collaborative security and operational platforms; and (4) efficient, tamper-evident storage and auditing suitable for next-generation smart grid requirements. Collectively, these contribu- tions pave the way for intelligent, secure, and sustainable energy systems addressing technical, economic, and social imperatives in real-world smart grid deployments.