Feature Weighting and Selection for a Real-Time Network Intrusion Detection System Based on GA with KNN

Authors:
Ming-Yang Su;Kai-Chi Chang;Hua-Fu Wei;Chun-Yuen Lin
Affiliations:
Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan Campus, Taiwan;Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan Campus, Taiwan;Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan Campus, Taiwan;Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan Campus, Taiwan
Venue:
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Year:
2008

Citing 5
Cited 1

Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence

Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks

SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
Protocol Analysis in Intrusion Detection Using Decision Tree

ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Network Intrusion Detection Through Genetic Feature Selection

SNPD-SAWN '06 Proceedings of the Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Decision tree classifier for network intrusion detection with GA-based feature selection

Proceedings of the 43rd annual Southeast regional conference - Volume 2

The use of artificial-intelligence-based ensembles for intrusion detection: a review

Applied Computational Intelligence and Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A good feature selection policy which can choose significant and as less as possible features plays a key role for any successful NIDS. The paper presents a genetic algorithm combined with kNN (k-Nearest Neighbor) for feature weighting. We weight all initial 35 features in the training phase and then select tops of them to implement a NIDS for testing. Many DoS/DDoS attacks are applied to evaluate the system. For known attacks we can get the best 97.42% overall accuracy rate while only the top 19 features are considered; as for unknown attacks, we can get the best 78% overall accuracy rate by top 28 features.