AMS Intrusion Detection Method Based on Improved Generalized Regression Neural Network

AMS Intrusion Detection Method Based on Improved Generalized Regression Neural Network

The smart grid integrates the computer network with the traditional power system and realizes the intelligentization of the power grid. The Advanced Measurement System (AMS) interconnects the power system with the user, realizes the two-way interaction of data and information between the power supplier and the user, and promotes the development of the smart grid. Therefore, the safe operation of AMS is the key to the development of the smart grid. As smart grids and computer networks become more and more closely connected, the number of cyberattacks on AMS continues to increase. Currently, AMS intrusion detection algorithms based on machine learning are constantly being proposed. Machine learning algorithms have better learning and classification capabilities for small sample data, but when faced with a large amount of high-dimensional data information, the learning ability of machine learning algorithms is reduced, and the generalization ability is reduced. To enhance the AMS intrusion detection algorithm, this paper uses a Generalized Regression Neural Network (GRNN) to identify attack behaviors. GRNN has strong non-linear mapping ability, is suitable for unstable data processing with small data characteristics, has good classification and prediction ability, and has been widely used in power grid systems. Aiming at the existing problems, this paper proposes an upgraded generalized regression neural network AMS intrusion detection method DBN-DOA-GRNN. Based on the feature extraction and dimensionality reduction of the data by DBN, GRNN is used for data with less feature information in learning classification. In addition, to improve the detection effect of the method, the Drosophila Optimization Algorithm (DOA) is used to optimize the parameters of GRNN to reduce the influence of random parameters on the detection results, improve the detection accuracy of this method on small-scale sample data, and thereby improve the detection performance of the AMS intrusion detection algorithm. The proposed method archives an accuracy of 87.61%, 3.10% false alarm rate, and 96.9 precision rate.