DMCA
Workshop on Information Assurance United States Military Academy, West Point, NY June 2001 Evolving Fuzzy Classifiers for Intrusion Detection
Cached
Download Links
BibTeX
@MISC{Gomez_workshopon,
author = {Jonatan Gomez and Dipankar Dasgupta},
title = {Workshop on Information Assurance United States Military Academy, West Point, NY June 2001 Evolving Fuzzy Classifiers for Intrusion Detection},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract – The normal and the abnormal behaviors in networked computers are hard to predict, as the boundaries cannot be well defined. This prediction process usually generates false alarms in many anomaly based intrusion detection systems. However, with fuzzy logic, the false alarm rate in determining intrusive activities can be reduced, where a set of fuzzy rules is used to define the normal and abnormal behavior in a computer network, and a fuzzy inference engine can be applied over such rules to determine intrusions. This paper proposes a technique (genetic algorithm) to generate fuzzy rules (instead of manual design) that are able to detect anomalies and some specific intrusions. Experiments were performed with DARPA data sets [1], which have information on computer networks, during normal behavior and intrusive behavior. This paper presents some results and reports the performance of generated fuzzy rules in classifying different types of intrusions. Index terms – Intrusion detection, fuzzy classification, rule generation, and genetic algorithms I.
Keyphrases
intrusion detection evolving fuzzy classifier ny june west point fuzzy rule computer network abnormal behavior genetic algorithm fuzzy classification specific intrusion many anomaly false alarm rate prediction process fuzzy logic intrusive activity index term intrusion detection darpa data set intrusion detection system manual design rule generation intrusive behavior normal behavior fuzzy inference engine different type false alarm networked computer generated fuzzy rule
