WPD-ResNeSt: Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer Learning
With the advancement of new infrastructures, the digitalization of the substation communication network has rapidly increased, and its information security risks have become increasingly prominent. Accurate and reliable substation communication network flow models and flow anomaly detection methods...
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| Format: | Article |
| Language: | English |
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China electric power research institute
2024-01-01
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| Series: | CSEE Journal of Power and Energy Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9465812/ |
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| _version_ | 1846113871888121856 |
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| author | Ting Yang Yucheng Hou Yachuang Liu Feng Zhai Rongze Niu |
| author_facet | Ting Yang Yucheng Hou Yachuang Liu Feng Zhai Rongze Niu |
| author_sort | Ting Yang |
| collection | DOAJ |
| description | With the advancement of new infrastructures, the digitalization of the substation communication network has rapidly increased, and its information security risks have become increasingly prominent. Accurate and reliable substation communication network flow models and flow anomaly detection methods have become an important means to prevent network security problems and identify network anomalies. The existing substation network analyzers and flow anomaly detection algorithms are usually based on threshold determination, which cannot reflect the inherent characteristics of substation automation flow based on IEC 61850 and have low detection accuracy. To effectively detect abnormal traffic, this paper fully explores the substation network traffic rules, extracts the frequency domain features of the station level network, and designs an abnormal traffic identification model based on the ResNeSt convolutional neural network. Transfer learning is used to solve the problem of insufficient abnormal traffic labeled samples in the substation. Finally, a new method of abnormal traffic detection in smart substation station level communication networks based on deep transfer learning is proposed. The T1-1 substation communication network is constructed on OPNET for abnormal simulations, and the actual network traffic in a 110kV substation is fused with CIC DDoS2019 and KDD99 data sets for the algorithm performance test, respectively. The accuracy reached is 98.73 % and 98.95 %, indicating that the detection model proposed in this paper has higher detection accuracy than existing algorithms. |
| format | Article |
| id | doaj-art-741218a81ff44d0db2bb23aea7bcfb51 |
| institution | Kabale University |
| issn | 2096-0042 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | China electric power research institute |
| record_format | Article |
| series | CSEE Journal of Power and Energy Systems |
| spelling | doaj-art-741218a81ff44d0db2bb23aea7bcfb512024-12-21T00:02:10ZengChina electric power research instituteCSEE Journal of Power and Energy Systems2096-00422024-01-011062610262010.17775/CSEEJPES.2020.028509465812WPD-ResNeSt: Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer LearningTing Yang0Yucheng Hou1Yachuang Liu2Feng Zhai3Rongze Niu4Key Laboratory of Smart Grid of Ministry of Education, Tianjin University,Tianjin,China,300072Key Laboratory of Smart Grid of Ministry of Education, Tianjin University,Tianjin,China,300072Key Laboratory of Smart Grid of Ministry of Education, Tianjin University,Tianjin,China,300072China Electric Power Research Institute,Beijing,China,100192Henan Electric Power Research Institute,Zhengzhou,China,450052With the advancement of new infrastructures, the digitalization of the substation communication network has rapidly increased, and its information security risks have become increasingly prominent. Accurate and reliable substation communication network flow models and flow anomaly detection methods have become an important means to prevent network security problems and identify network anomalies. The existing substation network analyzers and flow anomaly detection algorithms are usually based on threshold determination, which cannot reflect the inherent characteristics of substation automation flow based on IEC 61850 and have low detection accuracy. To effectively detect abnormal traffic, this paper fully explores the substation network traffic rules, extracts the frequency domain features of the station level network, and designs an abnormal traffic identification model based on the ResNeSt convolutional neural network. Transfer learning is used to solve the problem of insufficient abnormal traffic labeled samples in the substation. Finally, a new method of abnormal traffic detection in smart substation station level communication networks based on deep transfer learning is proposed. The T1-1 substation communication network is constructed on OPNET for abnormal simulations, and the actual network traffic in a 110kV substation is fused with CIC DDoS2019 and KDD99 data sets for the algorithm performance test, respectively. The accuracy reached is 98.73 % and 98.95 %, indicating that the detection model proposed in this paper has higher detection accuracy than existing algorithms.https://ieeexplore.ieee.org/document/9465812/Anomaly traffic detectiondeep learningsubstation station level communication networktraffic model |
| spellingShingle | Ting Yang Yucheng Hou Yachuang Liu Feng Zhai Rongze Niu WPD-ResNeSt: Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer Learning CSEE Journal of Power and Energy Systems Anomaly traffic detection deep learning substation station level communication network traffic model |
| title | WPD-ResNeSt: Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer Learning |
| title_full | WPD-ResNeSt: Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer Learning |
| title_fullStr | WPD-ResNeSt: Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer Learning |
| title_full_unstemmed | WPD-ResNeSt: Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer Learning |
| title_short | WPD-ResNeSt: Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer Learning |
| title_sort | wpd resnest substation station level network anomaly traffic detection based on deep transfer learning |
| topic | Anomaly traffic detection deep learning substation station level communication network traffic model |
| url | https://ieeexplore.ieee.org/document/9465812/ |
| work_keys_str_mv | AT tingyang wpdresnestsubstationstationlevelnetworkanomalytrafficdetectionbasedondeeptransferlearning AT yuchenghou wpdresnestsubstationstationlevelnetworkanomalytrafficdetectionbasedondeeptransferlearning AT yachuangliu wpdresnestsubstationstationlevelnetworkanomalytrafficdetectionbasedondeeptransferlearning AT fengzhai wpdresnestsubstationstationlevelnetworkanomalytrafficdetectionbasedondeeptransferlearning AT rongzeniu wpdresnestsubstationstationlevelnetworkanomalytrafficdetectionbasedondeeptransferlearning |