Network Traffic Anomaly Detection Based on ML-ESN for Power Metering System

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...

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Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-21
Main Authors: Liao, N. D., Song, Y. Q., Wu, L., Lin, X. B., Zhang, S. T., Liang, Z. H.
Format: Article
Language:English
Subjects:
Anomalies
Back propagation
Back propagation networks
Communication
Communications networks
Communications traffic
Complexity
Computer simulation
Datasets
Deep learning
Electricity
Electricity distribution
Flow characteristics
Machine learning
Mathematical problems
Monolayers
Multilayers
Neural networks
Platforms
Principal components analysis
Software
Statistical analysis
Virtual private networks
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/7219659