Software-Defined Network (SDN) can improve the performance of the power communication network and better meet the control demand of the Smart Grid for its centralized management. Unfortunately, the SDN controller is vulnerable to many potential network attacks. The accurate detection of abnormal flow is especially important for the security and reliability of the Smart Grid. Prior works were designed based on traditional machine learning methods, such as Support Vector Machine and Naive Bayes. They are simple and shallow feature learning, with low accuracy for large and high-dimensional network flow. Recently, there have been several related works designed based on Long Short-Term Memory (LSTM), and they show excellent ability on network flow analysis. However, these methods cannot get the deep features from network flow, resulting in low accuracy. To address the above problems, we propose a Hybrid Convolutional Neural Network (HYBRID-CNN) method. Specifically, the HYBRID-CNN utilizes a Deep Neural Network (DNN) to effectively memorize global features by one-dimensional (1D) data and utilizes a CNN to generalize local features by two-dimensional (2D) data. Finally, the proposed method is evaluated by experiments on the datasets of UNSW_NB15 and KDDCup 99. The experimental results show that the HYBRID-CNN significantly outperforms existing methods in terms of accuracy and False Positive Rate (FPR), which successfully demonstrates that it can effectively detect abnormal flow in the SDN-based Smart Grid.

中文翻译：

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HYBRID-CNN：基于SDN的智能电网中异常流量检测的有效方案

软件定义网络（SDN）可以提高电力通信网络的性能，并更好地满足智能电网对其集中管理的控制需求。不幸的是，SDN控制器容易受到许多潜在的网络攻击。准确检测异常流量对于智能电网的安全性和可靠性尤为重要。先前的作品是基于传统的机器学习方法（如支持向量机和朴素贝叶斯）设计的。它们是简单的浅层特征学习，对于大型和高维度的网络流，准确性较低。最近，有一些基于长期短期记忆（LSTM）设计的相关作品，它们在网络流分析方面显示出出色的能力。但是，这些方法无法从网络流中获取深层功能，因此准确性较低。为了解决上述问题，我们提出了一种混合卷积神经网络（HYBRID-CNN）方法。具体来说，HYBRID-CNN利用深度神经网络（DNN）通过一维（1D）数据有效存储全局特征，并利用CNN通过二维（2D）数据概括局部特征。最后，通过在UNSW_NB15和KDDCup 99数据集上的实验对提出的方法进行了评估。实验结果表明，HYBRID-CNN在准确性和误报率（FPR）方面明显优于现有方法，这成功证明了它可以有效地解决问题。检测基于SDN的智能电网中的异常流量。HYBRID-CNN利用深度神经网络（DNN）通过一维（1D）数据有效地存储全局特征，并利用CNN通过二维（2D）数据来概括局部特征。最后，通过在UNSW_NB15和KDDCup 99数据集上的实验对提出的方法进行了评估。实验结果表明，HYBRID-CNN在准确性和误报率（FPR）方面明显优于现有方法，这成功证明了它可以有效地解决问题。检测基于SDN的智能电网中的异常流量。HYBRID-CNN利用深度神经网络（DNN）通过一维（1D）数据有效地存储全局特征，并利用CNN通过二维（2D）数据来概括局部特征。最后，通过在UNSW_NB15和KDDCup 99数据集上的实验对提出的方法进行了评估。实验结果表明，HYBRID-CNN在准确性和误报率（FPR）方面明显优于现有方法，这成功证明了它可以有效地解决问题。检测基于SDN的智能电网中的异常流量。