| Abstract: |
As smart grids become increasingly interconnected and data-centric, they are susceptible to DDoS attacks, false data injection, and probing assaults. Traditional Intrusion Detection Systems (IDS) often struggle to identify these emerging threats due to the high-dimensional, dynamic, and imbalanced data they generate. To tackle these challenges, we present a novel hybrid deep learning model that combines Spatial-Temporal Graph Neural Networks (ST-GNNs) and Multi-Scale Transformers, integrated with an Adaptive Attention-Based Feature Fusion (AAFF) module. This approach enhances detection accuracy by revealing the intricate spatial and temporal correlations within network traffic data. The AAFF module dynamically adapts by prioritising the most relevant features, facilitating the swift detection of fraudulent activities. To enhance the model's ability to cope with atypical and novel threats, we employ contrastive self-supervised learning (CSSL), which boosts performance on imbalanced datasets. We incorporate dynamic graph generation, temporal node embedding, and Meta-Learning techniques to ensure the model remains flexible and adaptable to emerging attack patterns. A federated learning system is utilised for distributed detection across multiple grid locations, enhancing scalability and privacy. To enhance robustness, we employ Conditional Generative Adversarial Networks (CGANs) for data augmentation, allowing the model to generalise to previously unknown attack scenarios. Furthermore, we employ online active learning, enabling the model to respond to new data and attacks in real-time, ensuring prompt detection and response. We deploy the model on grid edge devices, minimising detection latency and facilitating quicker attack response times. When evaluated on well-known security datasets, such as CIC-DDoS2019, CIC-IDS2018, and CIC-DoS2017, the model achieves a detection accuracy of 98.42%, surpassing previous methods and significantly reducing false positives. The proposed strategy integrates spatial and temporal threat analysis, dynamic feature refinement, and adaptive detection methods to deliver a reliable and scalable solution for enhancing the cybersecurity of smart grids. [ABSTRACT FROM AUTHOR] |