Negative Selection Algorithm in Artificial Immune Systems

The Negative Selection Algorithm in artificial immune systems is an anomaly detection method inspired by biological immune mechanisms. This algorithm mimics the negative selection process that T-cells undergo in the thymus, where immune cells that incorrectly recognize self-tissues are eliminated, while only those capable of identifying foreign pathogens are preserved.

In anomaly detection applications, the algorithm first generates a set of detectors that are trained not to react to normal data patterns (thereby avoiding false positives). During system operation, any data matching these detectors is flagged as anomalous. This approach is particularly suitable for cybersecurity and industrial fault detection domains due to its adaptability to evolving normal behavior patterns.

The algorithm's core lies in efficiently generating detectors and determining matches through distance metrics (such as Hamming distance). Implementation typically involves defining a self-set representing normal patterns, then generating random candidate detectors that undergo negative selection against this self-set. Detectors surviving this process form the anomaly detection repertoire. Compared to traditional anomaly detection methods, the negative selection algorithm demonstrates superior adaptability, maintaining robust detection capabilities against emerging unknown threats.