Network Security White Papers
Bayesian Event Classification for Intrusion Detection
Overview Intrusion Detection Systems (IDSs) attempt to identify attacks by comparing collected data to predefined signatures known to be malicious (misuse-based IDSs) or to a model of legal behavior (anomaly-based IDSs). Anomaly-based approaches have the advantage of being able to detect previously unknown attacks, but they suffer from the difficulty of building robust models of acceptable behavior which may result in a large number of false alarms. Almost all current anomaly-based intrusion detection systems classify an input event as normal or anomalous by analyzing its features, utilizing a number of different models. A decision for an input event is made by aggregating the results of all employed models.
| Publisher | University of California | File Format | |
|---|---|---|---|
| Date Published | January 2008 | ||
| Format | White Papers | ||
| Topics | |||
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