An Adaptive Ensemble Machine Learning Model for Intrusion Detection | IEEE Journals & Magazine | IEEE Xplore

An Adaptive Ensemble Machine Learning Model for Intrusion Detection


Adaptive ensemble learning model.

Abstract:

In recent years, advanced threat attacks are increasing, but the traditional network intrusion detection system based on feature filtering has some drawbacks which make i...Show More
Topic: Artificial Intelligence in CyberSecurity

Abstract:

In recent years, advanced threat attacks are increasing, but the traditional network intrusion detection system based on feature filtering has some drawbacks which make it difficult to find new attacks in time. This paper takes NSL-KDD data set as the research object, analyses the latest progress and existing problems in the field of intrusion detection technology, and proposes an adaptive ensemble learning model. By adjusting the proportion of training data and setting up multiple decision trees, we construct a MultiTree algorithm. In order to improve the overall detection effect, we choose several base classifiers, including decision tree, random forest, kNN, DNN, and design an ensemble adaptive voting algorithm. We use NSL-KDD Test+ to verify our approach, the accuracy of the MultiTree algorithm is 84.2%, while the final accuracy of the adaptive voting algorithm reaches 85.2%. Compared with other research papers, it is proved that our ensemble model effectively improves detection accuracy. In addition, through the analysis of data, it is found that the quality of data features is an important factor to determine the detection effect. In the future, we should optimize the feature selection and preprocessing of intrusion detection data to achieve better results.
Topic: Artificial Intelligence in CyberSecurity
Adaptive ensemble learning model.
Published in: IEEE Access ( Volume: 7)
Page(s): 82512 - 82521
Date of Publication: 19 June 2019
Electronic ISSN: 2169-3536

Funding Agency:


References

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