基于卷积神经网络的工控网络异常流量检测

针对工控系统中传统的异常流量检测模型在识别异常上准确率不高的问题,提出一种基于卷积神经网络(CNN)的异常流量检测模型。该模型以卷积神经网络算法为核心,主要由1个卷积层、1个全连接层、1个dropout层以及1个输出层构成。首先,将实际采集的网络流量特征数值规约到与灰度图像素值相对应的范围内,生成网络流量灰度图;然后,将生成好的网络流量灰度图输入到设计好的卷积神经网络结构中进行训练和模型调优;最后,将训练好的模型用于工控网络异常流量检测。实验结果表明,所提模型识别精度达到97.88%,且与已有的精度最高反向传播(BP)神经网络测精度提高了5个百分点。

Abnormal flow monitoring of industrial control network based on convolutional neural network

Aiming at the inaccuracy of traditional abnormal flow detection model in the industrial control system, an abnormal flow detection model based on Convolutional Neural Network (CNN) was proposed. The proposed model was based on CNN algorithm and consisted of a convolutional layer, a full connection layer, a dropout layer and an output layer. Firstly, the actual collected network flow characteristic values were scaled to a range corresponding to the grayscale pixel values, and the network flow grayscale map was generated. Secondly, the generated network traffic grayscale image was put into the designed convolutional neural network structure for training and model tuning. Finally, the trained model was used to the abnormal flow detection of the industrial control network. The experimental results show that the proposed model has a recognition accuracy of 97.88%, which is 5 percentage points higher than that of Back Propagation (BP) neural network with the existing highest accuracy.