To read the full version of this content please select one of the options below:

Improving the performance of the intrusion detection systems by the machine learning explainability

Quang-Vinh Dang ( Industrial University of Ho Chi Minh City , Ho Chi Minh City, Vietnam)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 24 June 2021

Issue publication date: 21 September 2021




This study aims to explain the state-of-the-art machine learning models that are used in the intrusion detection problem for human-being understandable and study the relationship between the explainability and the performance of the models.


The authors study a recent intrusion data set collected from real-world scenarios and use state-of-the-art machine learning algorithms to detect the intrusion. The authors apply several novel techniques to explain the models, then evaluate manually the explanation. The authors then compare the performance of model post- and prior-explainability-based feature selection.


The authors confirm our hypothesis above and claim that by forcing the explainability, the model becomes more robust, requires less computational power but achieves a better predictive performance.


The authors draw our conclusions based on their own research and experimental works.



Dang, Q.-V. (2021), "Improving the performance of the intrusion detection systems by the machine learning explainability", International Journal of Web Information Systems, Vol. 17 No. 5, pp. 537-555.



Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited