Network Security White Papers
A Hierarchical Anomaly Network Intrusion Detection System Using Neural Network Classification
Overview This paper introduces a hierarchical anomaly network intrusion detection system, which is capable of detecting network - based attacks using statistical preprocessing models and neural network classification. The sample network used has a three-tier hierarchy, where the lower tier detectors report to the higher tiers. The statistical preprocessor converts network traffic sample information into a PDF that is compared to a historically developed PDF for corresponding normal network traffic, thus deriving a statistical similarity decision vector that the neural network classifies into anomalous (attack) or normal instance. Several simulation experiments have been carried out focusing on the Denial of Service attack.
| Publisher | New Jersey Institute of Technology | File Format | |
|---|---|---|---|
| Date Published | December 2000 | ||
| Format | White Papers | ||
| Topics | |||
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