Multiple classifier system with feature grouping for intrusion detection: mutual information approach

Authors:
Aki P. F. Chan;Wing W. Y. Ng;Daniel S. Yeung;Eric C. C. Tsang
Affiliations:
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2005

Citing 14
Cited 3

The Strength of Weak Learnability

Machine Learning
Genetic algorithm for feature selection for parallel classifiers

Information Processing Letters
Decision Combination in Multiple Classifier Systems

IEEE Transactions on Pattern Analysis and Machine Intelligence
The ordered weighted averaging operators: theory and applications

The ordered weighted averaging operators: theory and applications
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
The base-rate fallacy and the difficulty of intrusion detection

ACM Transactions on Information and System Security (TISSEC)
Practical Intrusion Detection Handbook

Practical Intrusion Detection Handbook
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Decimation Ensembles: Decorrelation through Dimensionality Reduction

MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Fusion of multiple classifiers for intrusion detection in computer networks

Pattern Recognition Letters
Intrusion detection using an ensemble of intelligent paradigms

Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Data mining approaches for intrusion detection

SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Intrusion detection techniques and approaches

Computer Communications

Empirical study on fusion methods using ensemble of RBFNN for network intrusion detection

ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Construction of high precision RBFNN with low false alarm for detecting flooding based denial of service attacks using stochastic sensitivity measure

ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Dynamic fusion method using Localized Generalization Error Model

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The information security of computer networks has become a serious global issue and also a hot topic in computer networking researches. Many approaches have been proposed to tackle these problems, especially the denial of service (DoS) attacks. The Multiple Classifier System (MCS) is one of the approaches that have been adopted in the detection of DoS attacks recently. For a MCS to perform better than a single classifier, it is crucial for the base classifiers which embedded in the MCS to be diverse. Both resampling, e.g. bagging, and feature grouping could promote diversity of base classifiers. In this paper, we propose an approach to select the reduced feature group for each of the base classifiers in the MCS based on the mutual information between the features and class labels. The base classifiers being combined using the weighted sum is examined in this paper. Different feature grouping methods are evaluated theoretically and experimentally.