Artificial immune system (AIS) is a diverse and maturing area of research that combines the disciplines of immunology and computation. Many researches focus on applying immunological principles to computer security. Negative selection algorithm (NSA) is one of the computational models of self/nonself discrimination can be designed for anomaly detection. It contains two stages: generate a set D of detectors that do not match any element of a given self-set S, then using these detectors to detect if a given cell is self or nonself. The performance of NSA often bases on the efficiency of generation and detection. Here, we present an r-chunk detector-based NSA that reduces the overall runtime complexity significantly. 

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