Network Security White Papers
Feature Deduction and Ensemble Design of Intrusion Detection Systems
Overview Current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building IDS that is computationally efficient and effective. This paper investigated the performance of two feature selection algorithms involving Bayesian Networks (BN) and Classification and Regression Trees CART) and an ensemble of BN and CART. Empirical results indicate that significant input feature selection is important to design an IDS that is lightweight, efficient and effective for real world detection systems. The paper proposes hybrid architecture for combining different feature selection algorithms for real world intrusion detection.
| Publisher | Reed Elsevier | File Format | |
|---|---|---|---|
| Date Published | September 2004 | ||
| Format | White Papers | ||
| Topics | |||
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