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An Advanced Accurate Intrusion Detection System for Smart Grid Cybersecurity Based on Evolving Machine Learning

Overview of attention for article published in Frontiers in Energy Research, May 2022
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
1 news outlet

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
17 Mendeley
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Title
An Advanced Accurate Intrusion Detection System for Smart Grid Cybersecurity Based on Evolving Machine Learning
Published in
Frontiers in Energy Research, May 2022
DOI 10.3389/fenrg.2022.903370
Authors

Tong Yu, Kai Da, Zhiwen Wang, Ying Ling, Xin Li, Dongmei Bin, Chunyan Yang

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 4 24%
Professor > Associate Professor 1 6%
Student > Ph. D. Student 1 6%
Unknown 11 65%
Readers by discipline Count As %
Computer Science 5 29%
Business, Management and Accounting 1 6%
Unknown 11 65%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 June 2022.
All research outputs
#15,255,201
of 22,684,168 outputs
Outputs from Frontiers in Energy Research
#559
of 3,158 outputs
Outputs of similar age
#242,677
of 437,020 outputs
Outputs of similar age from Frontiers in Energy Research
#31
of 432 outputs
Altmetric has tracked 22,684,168 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,158 research outputs from this source. They receive a mean Attention Score of 1.7. This one has done well, scoring higher than 80% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 437,020 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 432 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.