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Feature selection approach using ensemble learning for network anomaly detection

Content Provider	IET Digital Library
Author	Doreswamy Hooshmand, Mohammad Kazim Gad, Ibrahim
Abstract	Feature selection is essential for prioritising important attributes in data to improve prediction quality in machine learning algorithms. As different selection techniques identify different feature sets, relying on a single method may result in risky decisions. The authors propose an ensemble approach using union and quorum combination techniques with five primary individual selection methods which are analysis of variance, variance threshold, sequential backward search, recursive feature elimination, and least absolute selection and shrinkage operator. The proposed method reduces features in three rounds: (i) discard redundant features using pairwise correlation, (ii) individual methods select their own feature sets independently, and (iii) equalise individual feature sets. The equalised individual feature sets are combined using union and quorum techniques. Both the combined and individual sets are tested for network anomaly detection using random forest, decision tree, K-nearest neighbours, Gaussian Naive Bayes, and logistic regression classifiers. The experimental results on the UNSW-NB15 data set show that random forest with union and quorum feature sets yields 99 and 99.02% f1_score with minimum 6 and 12 features, respectively. The results on the NSL-KDD data set show that random forest with union and quorum gets 99.34 and 99.21% f1_score with a minimum of 28 and 18 features.
Starting Page	283
Ending Page	293
Page Count	11
Volume Number	5
e-ISSN	24682322
Issue Number	Issue 4, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/trit/5/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/trit.2020.0073
Journal	CAAI Transactions on Intelligence Technology
Publisher	The Institution of Engineering and Technology Chongqing University of Technology Chinese Association for Artificial Intelligence
Publisher Date	2020-10-15
Access Restriction	Open
Rights License	Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Subject Keyword	Absolute Selection Bayes Method Combinatorial Mathematics Data Mining Data Security Decision Tree Discard Redundant Features Ensemble Learning Equalise Individual Feature Sets Equalised Individual Feature Sets Feature Extraction Feature Selection Feature Selection Approach Individual Method Knowledge Engineering Technique Machine Learning Algorithm Network Anomaly Detection Pattern Classification Primary Individual Selection Method Quorum Feature Quorum Technique Random Forest Recursive Feature Elimination Regression Analysis Security of Data Shrinkage Operator Statistics Support Vector Machine
Content Type	Text
Resource Type	Article

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