Towards an artificial immune system for network intrusion detection: an investigation of dynamic clonal selection

One significant feature of artificial immune systems is their ability to adapt to continuously changing environments, dynamically learning the fluid patterns of ´self´ and predicting new patterns of ´non-self´. This paper introduces and investigates the behaviour of dynamiCS, a dynamic clonal selection algorithm, designed to have such properties of self-adaptation. The effects of three important system parameters: tolerisation period, activation threshold, and life span are explored. The abilities of dynamiCS to perform incremental learning on converged data, and to adapt to novel data are also demonstrated