DGA-PSO: An improved detector generation algorithm based on particle swarm optimization in negative selection

Abstract

The negative selection algorithm (NSA) is an essential algorithm in the artificial immune system used to achieve anomaly detection by generating detectors. The traditional NSA algorithm generates candidate detectors randomly, which leads to a partially dense and redundant distribution of detectors in the nonself areas, resulting in the presence of holes that are not covered by detectors. A detector generation algorithm based on particle swarm optimization (DGA-PSO) is proposed to overcome these defects. DGA-PSO converts the self-tolerance process into an adaptation function to guide particles to move in a specific direction by artificial settings and variants, generates efficient detectors covering the nonself space, reduces the redundancy among detectors and fills holes not covered. Thus, we successfully reduce the number of detectors while improving the detection rate of the algorithm. Through experimental validation analysis, DGA-PSO ranks first in detector training time and the detection rate on four UCI datasets compared to the classical algorithms RNSA and V-Detector and the improved algorithms BIORV-NSA, ADC-NSA and IFB-NSA.