An integrated fuzzy ants and artificial immune recognition system for anomaly detection
Date
2006
ISBN
8995003855; 978-899500385-5
Journal Title
Journal ISSN
Volume Title
Resource Type
Conference paper
Publisher
Journal Title
An integrated fuzzy ants and artificial immune recognition system for anomaly detection
Authors
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Abstract
A computer system intrusion is seen as any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. The introduction to networks and the internet caused great concern about the protection of sensitive information and have resulted in many computer security research efforts during the past few years. This paper highlights a novel approach for detecting intrusion based on bio-inspired algorithm. The intrusion detection model combines the fuzzy ants clustering algorithm and artificial immune recognition algorithm to maximize detection accuracy and minimize computational complexity. The implemented system has been tested on the training data set from DARPA DATA SET by MIT Lincoln Laboratory on intrusion. The applicability of the proposed method and the enhanced security it provides are discussed. © 2006 ICASE.
Description
Citation
2006 SICE-ICASE International Joint Conference