Research on Attack Detection Method of Microgrid Central Controller Based on Convolutional Neural Network

Abstract

The microgrid central controller (MGCC) integrates the functions of control, monitoring, and communication in microgrid system, and has powerful capabilities of information collection and data processing. However, with the development of microgrid system worldwide, the information security management capabilities of the MGCC are poor, If information / network attacks cannot be actively detected and identified, it will easily reduce the reliability of the microgird system operation. Attackers can use abnormal information or use the MGCC as a springboard to further attack the upper-layer system. Aiming at the above problems, this paper presents an attack detection method based on convolutional neural network, and a detailed design process of attack detection model of the MGCC is proposed. In the attack detection method, the important data streams in the MGCC are used as the input of the convolutional neural network model, then the convolutional neural network model detects or classifies these data streams, finally, intercept the data flow with attack behavior and give a warning prompt, and forward data without attack behaviors normally.

Convolutional neural network anomaly traffic detection based on DAPA

“…Reference [5] uses the selfattention model to analyze the correlation between different types of network traffic and realizes the anomaly detection and classification of traffic in the multi-feature network. With the increasing volume of network traffic, it is impossible to accurately detect abnormal traffic under unknown circumstances by relying on traffic characteristics [6].…”