An Intrusion Detection Method Based on Decision Tree-Recursive Feature Elimination in Ensemble Learning

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 learn...

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Vydáno v:Mathematical problems in engineering Ročník 2020; číslo 2020; s. 1 - 15
Hlavní autoři: Fan, Qi, Shi, Dandan, Jia, Bin, Nie, Guoqing, Lian, Wenjuan, Liang, Yongquan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Témata:
Accuracy
Algorithms
Artificial intelligence
Classification
Computer networks
Cybersecurity
Data encryption
Data processing
Datasets
Decision trees
Deep learning
Ensemble learning
Feature extraction
Information systems
Internet
Intrusion detection systems
Machine learning
Resource utilization
Security management
Security systems
Stacking
China
ISSN:1024-123X, 1563-5147
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Shrnutí: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%.
Bibliografie:ObjectType-Article-1
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ISSN:1024-123X
1563-5147
DOI:10.1155/2020/2835023