An Intrusion Detection Method Based on Decision Tree-Recursive Feature Elimination in Ensemble Learning
Joint Authors
Liang, Yongquan
Fan, Qi
Lian, Wenjuan
Nie, Guoqing
Jia, Bin
Shi, Dandan
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-23
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
With the rapid development of the Internet, various forms of network attack have emerged, so how to detect abnormal behavior effectively and to recognize their attack categories accurately have become an important research subject in the field of cyberspace security.
Recently, many hot machine learning-based approaches are applied in the Intrusion Detection System (IDS) to construct a data-driven model.
The methods are beneficial to reduce the time and cost of manual detection.
However, the real-time network data contain an ocean of redundant terms and noises, and some existing intrusion detection technologies have lower accuracy and inadequate ability of feature extraction.
In order to solve the above problems, this paper proposes an intrusion detection method based on the Decision Tree-Recursive Feature Elimination (DT-RFE) feature in ensemble learning.
We firstly propose a data processing method by the Decision Tree-Based Recursive Elimination Algorithm to select features and to reduce the feature dimension.
This method eliminates the redundant and uncorrelated data from the dataset to achieve better resource utilization and to reduce time complexity.
In this paper, we use the Stacking ensemble learning algorithm by combining Decision Tree (DT) with Recursive Feature Elimination (RFE) methods.
Finally, a series of comparison experiments by cross-validation on the KDD CUP 99 and NSL-KDD datasets indicate that the DT-RFE and Stacking-based approach can better improve the performance of the IDS, and the accuracy for all kinds of features is higher than 99%, except in the case of U2R accuracy, which is 98%.
American Psychological Association (APA)
Lian, Wenjuan& Nie, Guoqing& Jia, Bin& Shi, Dandan& Fan, Qi& Liang, Yongquan. 2020. An Intrusion Detection Method Based on Decision Tree-Recursive Feature Elimination in Ensemble Learning. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1194057
Modern Language Association (MLA)
Lian, Wenjuan…[et al.]. An Intrusion Detection Method Based on Decision Tree-Recursive Feature Elimination in Ensemble Learning. Mathematical Problems in Engineering No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1194057
American Medical Association (AMA)
Lian, Wenjuan& Nie, Guoqing& Jia, Bin& Shi, Dandan& Fan, Qi& Liang, Yongquan. An Intrusion Detection Method Based on Decision Tree-Recursive Feature Elimination in Ensemble Learning. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1194057
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194057