Ensemble of machine learning algorithms for intrusion detection

Ensemble-classifier is a technique that uses a combination of multiple classifiers to reach a more precise inference result than a single classifier. In this paper, a three-layer hierarchy multi-classifier intrusion detection architecture is proposed to promote the overall detection accuracy. For ma...

Full description

Saved in:
Bibliographic Details
Main Authors: Te-Shun Chou, Fan, J., Fan, S., Makki, K.
Format: Conference Proceeding
Language:English
Subjects:
Classification tree analysis
ensemble design
Feature extraction
feature selection
Intrusion detection
machine learning
Machine learning algorithms
Neural networks
Neurons
Performance evaluation
Probes
Testing
Training data
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Ensemble-classifier is a technique that uses a combination of multiple classifiers to reach a more precise inference result than a single classifier. In this paper, a three-layer hierarchy multi-classifier intrusion detection architecture is proposed to promote the overall detection accuracy. For making every individual classifier is independent from others, each uses a diverse soft computing technique as well as different feature subset. In addition, the performances of a variety of combination methods that fuse the outputs from classifiers are studied. In the experiments, DARPA KDD99 intrusion detection data set is chosen as the evaluation tools. The results show that our approach achieves a better performance than that of a single classifier.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2009.5346669