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Finding Danger using Fuzzy Dendritic Cells

This paper details a modification to the Dendritic Cell Algorithm so that residual and prior information about a potential attack may be used to infer the current state of a machine using soft computing techniques. Presented in this paper is the biological background needed along with the original algorithm, a deterministic form, and our modifications to create the Fuzzy Dendritic Cell algorithm. In the original form of the algorithm, an agent must stochastically sample an antigen until enough information is collected so that the agent may report itself as being mature or semi-mature. In the deterministic form, the agent must sample all dimensions of this antigen vector which deviates from the biological approach, yet provides a complete picture of an attack. While trying to model the human Dendritic Cell, our approach uses partial prior information from other agents, the information stochastically collected from a current agent, and successfully determines the agents maturity while reducing the amount of samples required to about 10% of the total dimensions in an antigen vector. This provides an approach similar to human Dendritic Cell, but also allows improves runtime costs when evaluating large dimensional antigen vectors over the original algorithm. Our approach also provides a way to incorporate traditional Artificial Immune System approaches such as Self, Non-Self Discrimination so that a complete Artificial Immune System may be realized.