Abstract
In order to overcome the blindness of the evaluation on contexts in the classical Dendritic Cell Algorithm (DCA), how weight matrixes influence detection results is analyzed, and two kinds of DCA which can adjust false positives and false negatives are proposed. The first one is the improved voting DCA, the Tendency Factor (TF) is involved in the Dendritic Cell (DC) state transition to assess contexts fairly, and through the fine adjustment of TF false positives and false negatives of detection results are controlled; the other one is the scoring DCA, in the DC state transition phase the evaluation of contexts is ignored, instead, the antigen is directly given a score, then according to the distribution of average scores of antigens the anomaly threshold value can be adjusted to control false positives and false negatives. Experiments show that the two algorithms can both effectively realize results controlled, comparatively the scoring DCA is more intuitive.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Greensmith, J., Aickelin, U., Twycross, J.: Articulation and clarification of the dendritic cell algorithm. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163, pp. 404–417. Springer, Heidelberg (2006)
Oates, R., Kendall, G., Garibaldi, J.: Frequency analysis for dendritic cell population tuning. Evol. Intel. 1(2), 145–157 (2009)
Greensmith, J., Aickelin, U.: Artificial Dendritic Cells: Multi-faceted Perspectives. In: Bargiela, A., Pedrycz, W. (eds.) Human-Centric Information Processing. SCI, vol. 182, pp. 375–395. Springer, Heidelberg (2009)
Greensmith, J., Aickelin, U., Tedesco, G.: Information fusion for anomaly detection with the dendritic cell algorithm. Inf. Fusion 11(1), 21–34 (2010)
Twycross, J.: Integrated innate and adaptive artificial immune systems applied to process anomaly detection. Ph.D. thesis. University of Nottingham (2007)
Ni, J.C., Li, Z.S., Sun, J.R., Zhou, L.P.: Research on differentiation model and application of dendritic cells in artificial immune system. Acta Electronica Sin. 36(11), 2210–2215 (2008)
Yang, C.X., Wu, G.F., Hu, M.: Improved dendritic cells algorithm. Comput. Eng. 35(23), 194–200 (2009)
Greensmith, J., Aickelin, U.: The deterministic dendritic cell algorithm. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 291–302. Springer, Heidelberg (2008)
Greensmith, J.: The dendritic cell algorithm. Ph.D. thesis. University of Nottingham (2007)
Greensmith, J., Aickelin, U., Cayzer, S.: Detecting danger: the dendritic cell algorithm. In: Schuster, A. (ed.) Robust Intelligent Systems, pp. 89–112. Springer, Heidelberg (2008)
Chen, Y.B., Feng, C., Zhang, Q., Tang, C.J.: Principles and application of dendritic cell algorithm. Comput. Eng. 36(8), 173–176 (2010)
Acknowledgement
This work was supported by the Natural Science Foundation of Hubei Provincial of China (2014CFB247), and the National Natural Science Foundation of China (No. 61440016).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yuan, S., Xu, X. (2015). Improved Dendritic Cell Algorithm with False Positives and False Negatives Adjustable. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-22180-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22179-3
Online ISBN: 978-3-319-22180-9
eBook Packages: Computer ScienceComputer Science (R0)