Abstract
An immune-inspired danger theory model based on dendritic cells (DCs) within the framework of fuzzy set theory is proposed in this paper. Our objective is to smooth the abrupt separation between normality (semi-mature) and abnormality (mature) using fuzzy set theory since we can neither identify a clear boundary between the two contexts nor quantify exactly what is meant by “semi-mature” or “mature”. In this model, the context of each object (DC) is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe these variables. A knowledge base, comprising rules, is built to support the fuzzy inference. The induction of the context of each object is diagnosed using a compositional rule of fuzzy inference. Experiments on real data sets show that by alleviating the crisp separation between the two contexts, our new approach which focuses on binary classification problems produces more accurate results.
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References
Aickelin, U., Bentley, P., Cayzer, S., Kim, J., McLeod, J.: Danger Theory: The Link between AIS and IDS. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 147–155. Springer, Heidelberg (2003)
Greensmith, J., Aickelin, U., Cayzer, S.: Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 153–167. Springer, Heidelberg (2005)
Ross, T.: Fuzzy Logic for Engineering Applications. McGraw-Hill Book Company, New York (1995)
UCI machine learning repository, http://archive.ics.uci.edu
Zadeh, L.: Fuzzy Sets. Information and Control 8, 338–353 (1965)
Zimmermann, J.: Fuzzy Set Theory and Its Applications. European Journal of Operational Research 1, 227–228 (1996)
Pedrycz, W., Zadeh, L.: Fuzzy Sets Engineering 1. IEEE Micro, 19–30 (1995)
Ishibuchi, H., Nakashima, T.: Effect of rule weights in fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems 9(4), 506–515 (2001)
Matzinger, P.: The Danger Model in its historical context. Scandinavian Journal of Immunology, 4–9 (2001)
Roberto, A., Myriam, D., Heitor, S., Alex, A.: An artificial immune system for fuzzy-rule induction in data mining. In: 8th Brazilian Symposium on Naturel Network, pp. 1011–1020 (2004)
Greensmith, J.: The Dendritic Cell Algorithm. PhD Thesis, University of Nottingham (2007)
Stibor, T.: On the appropriateness of negative selection for anomaly detection and network intrusion detection. PhD Thesis, Darmstadt University of Technology (2006)
Nauman, M., Muddassar, F.: A Sense of Danger: Dendritic Cells Inspired Artificial Immune System for MANET Security. In: Genetic and Evolutionary Computation Conference, pp. 102–109 (2007)
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 1–13 (1975)
Kyosev, Y., Peeva, K., Reinbach, I.: Max-Product Fuzzy Relational Equations as Inference Engine for Prediction of Textile Yarn Properties. In: 9th Fuzzy Days, pp. 18–20 (2006)
Mizumoto, M.: Fuzzy controls by product-sum gravity-method. Fuzzy Sets and Systems, c1.1–c1.4 (1990)
Van Broekhoven, E., De Baets, B.: Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions. Fuzzy sets and systems, 904–918 (2006)
Lee, C.: Fuzzy logic in control systems: Fuzzy logic controller - Parts 1 and 2. IEEE Transactions on Systems, Man and Cybernetics, 404–435 (1990)
Gu, F., Greensmith, J., Aickelin, U.: Further Exploration of the Dendritic Cell Algorithm: Antigen Multiplier and Time Windows. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 142–153. Springer, Heidelberg (2008)
Aickelin, U., Greensmith, J.: 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)
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Chelly, Z., Elouedi, Z. (2010). FDCM: A Fuzzy Dendritic Cell Method. In: Hart, E., McEwan, C., Timmis, J., Hone, A. (eds) Artificial Immune Systems. ICARIS 2010. Lecture Notes in Computer Science, vol 6209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14547-6_9
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DOI: https://doi.org/10.1007/978-3-642-14547-6_9
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