Generalized accumulation and detection method for false data injection attack of smart power grid

Abstract

The invention discloses a generalized accumulation and detection method for false data injection attacks of a smart power grid, and the method comprises the steps: building a discrete time dynamic model for state estimation of the smart power grid through employing an obtained power grid topological structure, a historical system state estimation value and line parameter data; adopting an iterative least square estimation method as an estimation method to estimate a system state in real time, adopting a fractional calculus theory to predict a state characteristic of a system at a next moment in combination with an estimated value of a power grid at a past moment, and on the basis, designing an efficient generalized accumulation and detection method by utilizing a predicted state value and an estimated state value, and detecting whether the system is attacked by false data injection in real time. The false data injection attack of the smart power grid can be detected, compared with the prior art, the detection precision is higher, the detection speed is higher, and safety control and stable operation of the power grid are ensured.

Description

technical field [0001] The invention belongs to the field of smart grid information security, and in particular relates to a generalized accumulation and detection method for false data injection attacks of smart grids. Background technique [0002] The smart grid achieves the goals of reliability, safety, economy, and high efficiency of the grid through advanced measurement and sensing equipment and advanced control methods. However, in recent years, frequent vicious network attacks at home and abroad have exposed the vulnerability of smart grids and sounded the alarm for strengthening the information security construction of smart grids. In particular, false data injection attacks covertly tamper with the measured values ​​and state variables of the power grid by exploiting the loopholes of bad detection methods in the power grid, seriously endangering the safe and stable operation of the power grid. Therefore, how to design an effective detection of false data injection ...

AI Technical Summary

Problems solved by technology

[0003] At present, the mainstream methods of false data injection attack detection in smart grid mainly include: (1) The detection method based on the principle of residual detection and the comparison with the set threshold, but the detection accuracy is low, the detection efficiency is low, and the false alarm rate is high. Defects; (2) Attack detection methods based on artificial intelligence such as deep learning and reinforcement learning, but the adjustable parameters of the artificial intelligence method itself are relatively complex, the calculation efficiency is low, and a large amount of data is required for training.

None of the existing technologies can fully meet the requirements for fast and accurate detection of false injection attacks on smart grids

[0004] The basic principle of the generalized accumulation and detection method is based on the sequential probability ratio test, using the current and recent process data to test the signal sequence with little change in the mean or variance, and has been relatively successful in network abnormal traffic detection and fault detection applications, but rarely used in smart grid false data injection attack detection

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