Information fusion for anomaly detection with the dendritic cell algorithm
Information Fusion
The application of a dendritic cell algorithm to a robotic classifier
ICARIS'07 Proceedings of the 6th 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
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
A Sense of `Danger' for Windows Processes
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Quiet in class: classification, noise and the dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Fault detection in analog circuits using a fuzzy dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
The danger theory applied to vegetal image pattern classification
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Further exploration of the fuzzy dendritic cell method
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Run-time malware detection based on positive selection
Journal in Computer Virology
COID-FDCM: the fuzzy maintained dendritic cell classification method
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
A transitional view of immune inspired techniques for anomaly detection
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
A new data pre-processing approach for the dendritic cellalgorithm based on fuzzy rough set theory
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A survey on optimization metaheuristics
Information Sciences: an International Journal
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The Dendritic Cell Algorithm is an immune-inspired algorithm originally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to analyse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm.