基于深度学习的电力信息网络流量异常检测

Traffic Anomaly Detection of Power Communication Networks Based on Deep Learning

随着信息技术的快速发展,通信、计算机和电网构成多功能复杂系统,通信设施的复杂化使智能电网网络安全问题日益严峻。为确保电力信息网络具有更高的安全性能,必须有效识别电力信息网络存在的入侵攻击。对此,提出了一种基于CNN(卷积神经网络)和LSTM(长短期记忆)网络的混合网络的异常检测方法,混合网络通过提取网络流量数据特征以获得较高的检测率,同时为减少模型训练样本中不同攻击类型样本数量不平衡对模型性能的影响,采用类别权重优化方法来提高模型鲁棒性。经实验证明,所提方法能够有效提高识别网络攻击的准确率。

With the rapid development of information technology, communication, computers and power grids constitute a multi-functional complex system. The complex communication facilities make network security of smart grid increasingly serious. Only by identifying intrusive attacks in power communication networks can higher safety performance be guaranteed. Therefore, the paper proposes a hybrid network anomaly detection method based on convolutional neural network(CNN) and long short-term memory(LSTM) network is proposed. The hybrid network obtains a high detection rate by extracting the characteristics of network traffic data. At the same time, the class weight optimization method is used to improve the robustness of the model to reduce the impact of the imbalanced number of different attack types on the model performance. The experimental results show that the method can effectively improve the accuracy of cyberattack identification.