Unsupervised network traffic anomaly detection with deep autoencoders
Logic Journal of the IGPL, ISSN: 1368-9894, Vol: 30, Issue: 6, Page: 912-925
2022
- 11Citations
- 30Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Contemporary Artificial Intelligence methods, especially their subset-deep learning, are finding their way to successful implementations in the detection and classification of intrusions at the network level. This paper presents an intrusion detection mechanism that leverages Deep AutoEncoder and several Deep Decoders for unsupervised classification. This work incorporates multiple network topology setups for comparative studies. The efficiency of the proposed topologies is validated on two established benchmark datasets: UNSW-NB15 and NetML-2020. The results of their analysis are discussed in terms of classification accuracy, detection rate, false-positive rate, negative predictive value, Matthews correlation coefficient and F1-score. Furthermore, comparing against the state-of-the-art methods used for network intrusion detection is also disclosed.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85160751159&origin=inward; http://dx.doi.org/10.1093/jigpal/jzac002; https://academic.oup.com/jigpal/article/30/6/912/6534146; https://dx.doi.org/10.1093/jigpal/jzac002; https://academic.oup.com/jigpal/article-abstract/30/6/912/6534146?redirectedFrom=fulltext
Oxford University Press (OUP)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know