Abnormal Operation Detection of Access Equipment in Dispatching Exchange Network Based on Data Mining. Issue 1 (1st February 2023)
- Record Type:
- Journal Article
- Title:
- Abnormal Operation Detection of Access Equipment in Dispatching Exchange Network Based on Data Mining. Issue 1 (1st February 2023)
- Main Title:
- Abnormal Operation Detection of Access Equipment in Dispatching Exchange Network Based on Data Mining
- Authors:
- Zhai, Hongting
Zhang, Qingrui
Sun, Lili
Zhang, Yantong
Zhai, Qi - Abstract:
- Abstract: To meet the requirements of a power dispatching service, a dispatching switching network has been widely used instead of circuit switching technology. However, with the growth of the number of access devices, the problem of network security is becoming more and more serious. At present, the commonly used access equipment abnormal operation detection method mainly relies on a two-stage clustering method to identify the abnormal operation state, but the processing effect is not good in the face of a large number of data, resulting in a low F1 value of the final detection result. Therefore, aiming at the access equipment of the dispatching exchange network, an abnormal operation detection method based on data mining is proposed. Scapy, a network tool, is used to capture the traffic data of access devices, and the collected data are normalized and fused. Data feature information is extracted through time sliding window and incremental calculation mode. With the clustering algorithm and related rules in data mining as the core, the abnormal operation detection method of access equipment is designed to obtain accurate abnormal detection results. Finally, based on the recursive graph structure, the detection results are visualized. The experimental results show that the average F1 value of the detection results of the proposed method is 0. 98, which indicates that the detection results of the proposed method have higher accuracy, and based on this, the network securityAbstract: To meet the requirements of a power dispatching service, a dispatching switching network has been widely used instead of circuit switching technology. However, with the growth of the number of access devices, the problem of network security is becoming more and more serious. At present, the commonly used access equipment abnormal operation detection method mainly relies on a two-stage clustering method to identify the abnormal operation state, but the processing effect is not good in the face of a large number of data, resulting in a low F1 value of the final detection result. Therefore, aiming at the access equipment of the dispatching exchange network, an abnormal operation detection method based on data mining is proposed. Scapy, a network tool, is used to capture the traffic data of access devices, and the collected data are normalized and fused. Data feature information is extracted through time sliding window and incremental calculation mode. With the clustering algorithm and related rules in data mining as the core, the abnormal operation detection method of access equipment is designed to obtain accurate abnormal detection results. Finally, based on the recursive graph structure, the detection results are visualized. The experimental results show that the average F1 value of the detection results of the proposed method is 0. 98, which indicates that the detection results of the proposed method have higher accuracy, and based on this, the network security isolation is carried out, which effectively improves the security of dispatching switching network. … (more)
- Is Part Of:
- Journal of physics. Volume 2427:Issue 1(2023)
- Journal:
- Journal of physics
- Issue:
- Volume 2427:Issue 1(2023)
- Issue Display:
- Volume 2427, Issue 1 (2023)
- Year:
- 2023
- Volume:
- 2427
- Issue:
- 1
- Issue Sort Value:
- 2023-2427-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-02-01
- Subjects:
- Physics -- Congresses
530.5 - Journal URLs:
- http://www.iop.org/EJ/journal/1742-6596 ↗
http://ioppublishing.org/ ↗ - DOI:
- 10.1088/1742-6596/2427/1/012014 ↗
- Languages:
- English
- ISSNs:
- 1742-6588
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5036.223000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26020.xml