基于宽度学习的智能电网数据服务器流量异常检测算法

电力系统的信息网络是电力行业长久持续有效运行下的重要组成部分，而智能电网中电力网与信息网耦合下的复杂网络结构给信息通讯网络安全中的流量异常检测带来了巨大的挑战。传统机器学习算法与新兴的深度学习算法在解决流量异常检测问题领域往往存在着检测准确度低、实时性差等缺陷，而结合宽度学习与质量管理图的流量异常检测流程则有着训练速度快、准确性高、实时性强的优势，在一定程度上可以满足智能电网服务器流量异常检测需求，从而达到提升电网信息安全的目的。

An Anomaly Detection Approach on Servers Traffic in Smart #br# Grid Based on Breadth Learning Algorithm

The information network of the power system is an important part of the long-term continuous and effective operation in power industry. The complex network structure between power network and information network in the smart grid brings great challenges to the anomaly detection on network flow in information communication network security. Traditional machine learning algorithms and newly developing deep learning algorithms often have shortcomings such as low detection accuracy and poor real-time performance in solving the problem of network flow anomaly detection, while the network anomaly detection process that combines breadth learning and control chart has the advantages of faster training speed and more accurate detecting results. These advantages can meet the needs of anomaly detection requirement in smart grid to a certain extent, thereby achieving the purpose of improving the security of information network.