An abnormal traffic detection method in smart substations based on coupling field extraction and DBSCAN Article Swipe
Jianwei Tian
,
Zongchao Yu
,
Li Liu
,
Weidong Wu
,
Hongyu Zhu
,
Xuan Liu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1051/e3sconf/202126002005
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1051/e3sconf/202126002005
Smart Substation becomes more vulnerable to cyber attacks due to the high integration of information technologies, so it is essential to detect intrusion behaviour by abnormal traffic analysis in smart substations. Although there have been many detection methods for abnormal traffic, the existing ones all focus on the format check of a single field of the industrial transmission protocol, and ignore the deep coupling relationships among multiple protocol fields, which lead to more or less false detections and missed detections. To overcome this problem and further improve the detection accuracy, in this paper, we propose an abnormal traffic detection method based on the coupling field extraction and the density-based spatial clustering of applications with noise (DBSCAN). By using correlation analysis to extract the coupling fields of the protocol fields and using DBSCAN to remove the noise in the coupling fields, the deep coupling relationship between the coupling fields can be mined by the piecewise linear function fitting method, and used to detect abnormal traffic. The simulation results on 10,000 frames traffic prove that the proposed detection method can effectively identify the abnormal traffic.
Related Topics
Concepts
DBSCAN
Computer science
Noise (video)
Field (mathematics)
Coupling (piping)
Data mining
Cluster analysis
Anomaly detection
Intrusion detection system
Protocol (science)
Piecewise
Pattern recognition (psychology)
Artificial intelligence
Real-time computing
Engineering
Mathematics
Image (mathematics)
Fuzzy clustering
Mathematical analysis
Alternative medicine
Medicine
Mechanical engineering
Canopy clustering algorithm
Pathology
Pure mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/e3sconf/202126002005
- https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/36/e3sconf_aepee2021_02005.pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3163834216
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3163834216Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1051/e3sconf/202126002005Digital Object Identifier
- Title
-
An abnormal traffic detection method in smart substations based on coupling field extraction and DBSCANWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Jianwei Tian, Zongchao Yu, Li Liu, Weidong Wu, Hongyu Zhu, Xuan LiuList of authors in order
- Landing page
-
https://doi.org/10.1051/e3sconf/202126002005Publisher landing page
- PDF URL
-
https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/36/e3sconf_aepee2021_02005.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/36/e3sconf_aepee2021_02005.pdfDirect OA link when available
- Concepts
-
DBSCAN, Computer science, Noise (video), Field (mathematics), Coupling (piping), Data mining, Cluster analysis, Anomaly detection, Intrusion detection system, Protocol (science), Piecewise, Pattern recognition (psychology), Artificial intelligence, Real-time computing, Engineering, Mathematics, Image (mathematics), Fuzzy clustering, Mathematical analysis, Alternative medicine, Medicine, Mechanical engineering, Canopy clustering algorithm, Pathology, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
15Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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