A Negative Selection Approach to Intrusion Detection
An negative selection algorithm is presented for intrusion detection tasks for systems with arbitrary diversity. This algorithm uses two types of agents, detectors and presenters. Presenters present information to detectors; detectors are selected to engage in a maximally frustrated dynamics when presenters present data from a reference state. We show that if presenters present information that has never been available during the selection stage, then presenters engage in a less frustrated dynamics and their abnormal presentation can be detected. The performance of our algorithm is independent of the dimension of the space, i.e., the length of information presented by presenters, and hence does not suffer from the dimensionality curse accompanying current methods.
Keywordsartificial immune systems self/nonself discrimination negative selection algorithm
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