Evolving boundary detector for anomaly detection

In real-valued negative selection algorithm, the variability of self sample would result in the holes on the boundary between the self and non-self region and the deceiving anomalies hidden in the self region. This paper analyzes the reason for the difficulty in handling these problems by traditional evolved detectors, and then proposes a method of evolving boundary detectors to solve them. This method uses an improved detector generation algorithm based on evolutionary search to generate boundary detectors. The boundary detectors constructed by an aggressive interpretation are allowed to cover a part of self region. The aggressiveness controlled by boundary threshold can convert some volume of self sample into the fitness of boundary detector. This makes them enable to eliminate the holes on the boundary and have an opportunity to detect the deceiving anomalies hidden in the self region. Experiments are carried out using both 2-dimensional dataset and real world dataset. The former was designed to demonstrate intuitively that boundary detectors can cover the holes on the boundary, while the latter was to show that boundary detectors can detect the deceiving anomalies.