Due to the diversity and complexity of power network system platforms, some traditional network traffic detection methods work well for small sample datasets. However, the network data detection of complex power metering system platforms has problems of low accuracy and high false-positive rate. In this paper, through a combination of exploration and feedback, a solution for power network traffic anomaly detection based on multilayer echo state network (ML-ESN) is proposed. This method first relies on the Pearson and Gini coefficient method to calculate the statistical distribution and correlation of network flow characteristics and then uses the ML-ESN method to classify the network attacks abnormally. Because the ML-ESN method abandons the backpropagation mechanism, the nonlinear fitting ability of the model is solved. In order to verify the effectiveness of the proposed method, a simulation test was conducted on the UNSW_NB15 network security dataset. The test results show that the average accuracy of this method is more than 97%, which is significantly better than single-layer echo state network, shallow BP neural network, and some traditional machine learning methods.
With the continuous development of smart grids, communication networks carry more and more power services, and at the same time, they are also facing more and more security issues. For example, some malicious software usually uses encryption technology or tunnel technology to bypass firewalls, intrusion detection systems, etc., thereby posing a serious threat to the information security of smart grids. At present, the classification of network traffic mainly depends on the correct extraction of network protocol characteristics. However, the process of extracting network features by some traditional methods is time-consuming and overly dependent on experience. In order to solve the problem of accurate classification of power network traffic, this paper proposes a method of convolutional neural network based on genetic algorithm optimization (GACNN) and data statistical analysis. This method can simultaneously extract the time characteristics between different packet groups and the spatial characteristics in the same packet group. Therefore, it greatly saves manpower and gets rid of the dependence on experience value. The proposed method has been tested and verified on the UNSW-NB15 dataset and the real dataset collected by the power company. The results show that the proposed method can correctly classify abnormal network flows and is much better than traditional machine learning methods. In large-scale real network flow scenarios, the detection rate of the proposed method exceeds 97%, while the traditional method is generally less than 90%.
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