Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach (2209.00778v2)

Abstract: As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grid. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verifed.

• The paper proposes a secure federated deep learning approach combining federated learning, Transformer models, and the Paillier cryptosystem to detect False Data Injection Attacks (FDIAs) in smart grids while preserving data privacy.
• Experimental results on IEEE test systems demonstrate the method's superior accuracy and robustness against various FDIAs and noise interference compared to traditional CNN and LSTM approaches.
• The implications include a scalable, privacy-preserving solution for real-time FDIA detection in large smart grids, setting a precedent for AI-driven security in cyber-physical systems.

Overview of Federated Deep Learning in FDIA Detection in Smart Grids

The paper "Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach" presents a novel technique for enhancing cybersecurity in smart grids. It focuses on False Data Injection Attacks (FDIAs), a significant threat to the reliability of smart grids. The proposed approach combines federated learning with the Transformer model to address the challenges of privacy preservation and the increasing complexity of smart grid systems.

Technical Contributions

This paper introduces a federated learning-based framework to collaboratively train FDIA detection models across various nodes in the smart grid while ensuring data privacy. Federated learning enables nodes to locally train their models and share only the model updates, not raw data, thus protecting sensitive information.

The researchers deploy a Transformer-based model at each node for local FDIA detection, which is notable for its ability to capture complex, non-linear relationships within the data using self-attention mechanisms. The inclusion of a Paillier cryptosystem further secures the federated learning process by encrypting model updates, protecting against potential cyber threats that might exploit model weights to deduce original data.

Key Numerical Results and Comparisons

Experiments using the IEEE 14-bus and 118-bus test systems demonstrate the effectiveness of the proposed method. Under strong attacks, the Transformer-based detection model exhibits superior accuracy and robustness compared to traditional methods like CNN and LSTM. For instance, in the IEEE 14-bus system, detection accuracy exceeds 90\% across various communication rounds and types of attacks.

The robustness of the approach is further validated by adding Gaussian noise to simulate real-world measurement inaccuracies. The method maintains high detection performance despite noise interference, highlighting its resistance to potential signal disturbances common in practical applications.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the secure federated deep learning model offers a scalable solution for FDIA detection in large, distributed smart grid networks. The use of federated learning mitigates the latency issues and privacy concerns associated with centralized data processing and enhances the system's capability to handle real-time threats efficiently.

Theoretically, integrating advanced deep learning models like Transformers with privacy-preserving techniques sets a precedent for future developments in cybersecurity within cyber-physical systems. It opens pathways for additional research in adapting homomorphic encryption methods to further enhance data security in decentralized networks.

Future explorations could extend this framework to address multi-dimensional cyber threats beyond FDIAs or incorporate automated machine learning techniques for optimizing model hyperparameters. Exploring asymmetric threats with incomplete information would also present an interesting challenge for researchers aiming to fortify smart grid security comprehensively.

In conclusion, this paper provides a substantial contribution to the cybersecurity domain of smart grids by proposing a secure, decentralized approach for FDIA detection. The successful synergy of federated learning and Transformers, fortified by cryptographic techniques, exemplifies the potential advancements in AI-driven security strategies.