Combining negative selection and classification techniques for anomaly detection
This paper presents a novel approach inspired by the immune system that allows the application of conventional classification algorithms to perform anomaly detection. This approach appears to be very useful where only positive samples are available to train an anomaly detection system. The proposed approach uses the positive samples to generate negative samples that are used as training data for a classification algorithm. In particular, the algorithm produces fuzzy characterization of the normal (or abnormal) space. This allows it to assign a degree of normalcy, represented by membership value, to elements of the space.