الفهرس | Only 14 pages are availabe for public view |
Abstract Creating an efficient, green, and multifunctional smart grid (SG) cyber-physical system (CPS) while maintaining high dependability and security is a difficult undertaking, especially in today’s ever-changing cyber threat scenarios. This problem is exacerbated by the rising pervasiveness of information and communication technology throughout the electrical infrastructure, as well as the increased availability of advanced hacking tools in the hacker community. False data injection attacks (FDIAs) have recently been identified as one of the most critical security threats in SG. In this thesis a comparative analysis of several deep learning models has been conducted. This comparative analysis in order to identify the best multilabel classifier for locating locations of FDIAs with precise detection accuracy. Also a Real-Time multivariate based multi-label locational detection mechanism (MMLD) is proposed to detect the presence and locations of FDIAs. In this thesis two detection approaches based on MMLD mechanism are proposed. The suggested approaches called a Multi-Feature based Convolutional Neural Network and Long Short Term Memory (MCNN-LSTM) and LSTM-TCN. The LSTM-TCN architecture concatenates Long Short Term Memory (LSTM) with Temporal Convolutional Neural Network (TCN). Augmenting the TCN Blocks with LSTM block enhanced the performance of MMLD and increased locational classification accuracy. Furthermore, when the features of the LSTM block are combined with those of TCN, we get a more robust set of features that can better distinguish the FDIA multi-label classes. The advantageous performance of the proposed architectures are verified in IEEE standard bus system test cases. Extensive testing reveals that the proposed techniques have a modest advantage in some aspects. First, our mechanisms outperforms benchmark models for locating stealthy FDIAs in small and large systems under various attack conditions. Second, they need fewer iterations for training and reaching the optimal models. More specifically, our approaches are less complex and more scalable than benchmark algorithms. In addition, we provide a customized loss function for handling the unbalanced dataset. |