A feature selection approach to find optimal feature subsets for the network intrusion detection system

The performance of network intrusion detection systems based on machine learning techniques in terms of accuracy and efficiency largely depends on the selected features. However, choosing the optimal subset of features from a number of commonly used features to detect network intrusion requires exte...

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Bibliographic Details
Published in:Cluster computing 2016-03, Vol.19 (1), p.325-333
Main Authors: Kang, Seung-Ho, Kim, Kuinam J.
Format: Article
Language:English
Subjects:
Algorithms
Boolean
Cluster analysis
Clustering
Computer Communication Networks
Computer Science
Cost function
Datasets
Denial of service attacks
Feature selection
Heuristic methods
Intrusion detection systems
Machine learning
Multilayer perceptrons
Multilayers
Operating Systems
Performance evaluation
Processor Architectures
Search algorithms
Security management
Vector quantization
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Summary:The performance of network intrusion detection systems based on machine learning techniques in terms of accuracy and efficiency largely depends on the selected features. However, choosing the optimal subset of features from a number of commonly used features to detect network intrusion requires extensive computing resources. The number of possible feature subsets from given n features is 2 n - 1 . In this paper, to tackle this problem we propose an optimal feature selection algorithm. Proposed algorithm is based on a local search algorithm, one of the representative meta-heuristic algorithms for solving computationally hard optimization problems. Particularly, the accuracy of clustering obtained by applying k -means clustering algorithm to the training data set is exploited to measure the goodness of a feature subset as a cost function. In order to evaluate the performance of our proposed algorithm, comparisons with a feature set composed of all 41 features are carried out over the NSL-KDD data set using a multi-layer perceptron.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-015-0527-8