V-detector: An efficient negative selection algorithm with “probably adequate” detector coverage

This paper describes an enhanced negative selection algorithm (NSA) called V-detector. Several key characteristics make this method a state-of-the-art advance in the decade-old NSA. First, individual-specific size (or matching threshold) of the detectors is utilized to maximize the anomaly coverage at little extra cost. Second, statistical estimation is integrated in the detector generation algorithm so the target coverage can be achieved with given probability. Furthermore, this algorithm is presented in a generic form based on the abstract concepts of data points and matching threshold. Hence it can be extended from the current real-valued implementation to other problem space with different distance measure, data/detector representation schemes, etc. By using one-shot process to generate the detector set, this algorithm is more efficient than strongly evolutionary approaches. It also includes the option to interpret the training data as a whole so the boundary between the self and nonself areas can be detected more distinctly. The discussion is focused on the features attributed to negative selection algorithms instead of combination with other strategies.