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Apply machine learning techniques to detect malicious network traffic in cloud computing

Overview of attention for article published in Journal of Big Data, June 2021
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Mentioned by

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1 X user

Citations

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33 Dimensions

Readers on

mendeley
116 Mendeley
Title
Apply machine learning techniques to detect malicious network traffic in cloud computing
Published in
Journal of Big Data, June 2021
DOI 10.1186/s40537-021-00475-1
Authors

Amirah Alshammari, Abdulaziz Aldribi

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 116 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 10%
Student > Ph. D. Student 11 9%
Lecturer 7 6%
Student > Bachelor 4 3%
Researcher 4 3%
Other 13 11%
Unknown 65 56%
Readers by discipline Count As %
Computer Science 39 34%
Engineering 7 6%
Agricultural and Biological Sciences 1 <1%
Biochemistry, Genetics and Molecular Biology 1 <1%
Energy 1 <1%
Other 1 <1%
Unknown 66 57%
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 2021.
All research outputs
#20,707,815
of 23,308,124 outputs
Outputs from Journal of Big Data
#335
of 351 outputs
Outputs of similar age
#367,542
of 446,909 outputs
Outputs of similar age from Journal of Big Data
#14
of 14 outputs
Altmetric has tracked 23,308,124 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 351 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 446,909 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.