Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a...

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Vydané v:IEEE transactions on computers Ročník 65; číslo 10; s. 2986 - 2998
Hlavní autori: Ambusaidi, Mohammed A., Xiangjian He, Nanda, Priyadarsi, Zhiyuan Tan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Classification
Correlation
Drafting
Feature extraction
feature selection
Intrusion detection
Intrusion detection systems
least square support vector machine
linear correlation coefficient
Mutual information
Random variables
Support vector machines
ISSN:0018-9340, 1557-9956
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Shrnutí:Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9340
1557-9956
DOI:10.1109/TC.2016.2519914