Machine Learning
KDD-99 classifier learning contest LLSoft's results overview
ACM SIGKDD Explorations Newsletter
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Applicability issues of the real-valued negative selection algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Dendritic cells for SYN scan detection
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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
Exploration of the Dendritic Cell Algorithm Using the Duration Calculus
ICARIS '09 Proceedings of the 8th 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
Rethinking concepts of the dendritic cell algorithm for multiple data stream analysis
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
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As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performance in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DCA, including antigen multiplier and moving time windows, are reported. The real-valued Negative Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision tree algorithm are used, to conduct a baseline comparison. The results suggest that the DCA is applicable to KDD 99 data set, and the antigen multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with contant-sized detectors is not applicable to the data set. And the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.