ARTIFICIAL IMMUNE SYSTEMS IN THE CONTEXT OF MISBEHAVIOR DETECTION

Authors:
Sven Schaust;Helena Szczerbicka
Affiliations:
Leibniz University of Hannover, Hannover, Germany;Leibniz University of Hannover, Hannover, Germany
Venue:
Cybernetics and Systems
Year:
2008

Citing 6
Cited 3

Artificial Immune Systems: A New Computational Intelligence Paradigm

Artificial Immune Systems: A New Computational Intelligence Paradigm
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications

SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Kolmogorov complexity estimates for detection of viruses in biologically inspired security systems: a comparison with traditional approaches

Complexity - Special issue: Resilient and adaptive defense of computing networks
Danger is ubiquitous: detecting malicious activities in sensor networks using the dendritic cell algorithm

ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Articulation and clarification of the dendritic cell algorithm

ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Advances in artificial immune systems

IEEE Computational Intelligence Magazine

The Pathways of Complement

ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
A TOOL FOR PROTOTYPING AIS BASED PROTECTION SYSTEMS FOR AD HOC AND SENSOR NETWORKS

Cybernetics and Systems
Goals and Practices in Maintaining Information Systems Security

International Journal of Information Security and Privacy

Quantified Score

Hi-index	0.00

Visualization

Abstract

Artificial immune systems (AIS) are one of the most recent approaches in computational intelligence. This emerging field is sometimes referred to as immunological computation. The development of AIS is fostered by the advances made in understanding the complex mechanisms of the biological immune system. They provide efficient and robust information processing capabilities for solving complex problems. They can learn, adapt previously learned information, and perform pattern recognition/classification in a distributed way. This article discusses AIS especially in the context of misbehavior detection in wireless ad hoc (sensor) networks.