Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Ad-hoc On-Demand Distance Vector Routing
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
BeeAdHoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behavior
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Ariadne: a secure on-demand routing protocol for ad hoc networks
Wireless Networks
Vulnerability analysis and security framework (BeeSec) for nature inspired MANET routing protocols
Proceedings of the 9th annual conference on Genetic and evolutionary computation
BeeAIS: artificial immune system security for nature inspired, MANET routing protocol, BeeADHoc
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
IEEE Transactions on Neural Networks
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
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AIS-based intrusion detection systems classically utilize the paradigm of self/non-self discrimination. In this approach, an algorithm learns self during a learning phase, therefore, such algorithms do not have the ability to cope with scenarios in which self is continuously changing with time. This situation is encountered once malicious nodes are to be detected in a Mobile Ad Hoc Network (MANET). Consequently, it becomes a challenge to differentiate a valid route change due to mobility from an illegal one due to tampering of routing information by malicious nodes. In this paper, we propose a dendritic cell based distributed misbehavior detection system, BeeAIS-DC, for a Bio/Nature inspired MANET routing protocol, BeeAdHoc. Our proposed system inspires from the danger theory and models the function and behavior of dendritic cells to detect the presence or absence of danger and provides a tolerogenic or immunogenic response. The proposed detection system is implemented in a well-known ns-2 simulator. Our results indicate that our detection system not only enables BeeAIS-DC to dynamically adapt its detector set to cater for a changing self due to mobility of nodes, but also is robust enough to provide significantly smaller false positives as compared to self/non-self based AIS. Moreover, the danger theory related overhead of BeeAIS-DC is minimal, and as a result, it does not degrade traditional performance metrics of BeeAdHoc. This behavior is vital for battery/bandwidth constrained mobile nodes.