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Machine learning for intrusion detection in industrial control systems: challenges and lessons from experimental evaluation

Overview of attention for article published in Cybersecurity, August 2021
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)

Mentioned by

twitter
12 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
73 Mendeley
Title
Machine learning for intrusion detection in industrial control systems: challenges and lessons from experimental evaluation
Published in
Cybersecurity, August 2021
DOI 10.1186/s42400-021-00095-5
Authors

Gauthama Raman M. R., Chuadhry Mujeeb Ahmed, Aditya Mathur

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 8%
Researcher 6 8%
Student > Bachelor 3 4%
Professor 3 4%
Student > Ph. D. Student 3 4%
Other 5 7%
Unknown 47 64%
Readers by discipline Count As %
Computer Science 16 22%
Engineering 6 8%
Social Sciences 2 3%
Business, Management and Accounting 1 1%
Unknown 48 66%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 15 September 2021.
All research outputs
#6,692,728
of 22,489,892 outputs
Outputs from Cybersecurity
#13
of 38 outputs
Outputs of similar age
#120,489
of 343,789 outputs
Outputs of similar age from Cybersecurity
#1
of 1 outputs
Altmetric has tracked 22,489,892 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 38 research outputs from this source. They receive a mean Attention Score of 4.9. This one scored the same or higher as 25 of them.
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 343,789 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them