A population-based incremental learning approach with artificial immune system for network intrusion detection

The focus of this research is to develop a classifier using an artificial immune system (AIS) combined with population-based incremental learning (PBIL) and collaborative filtering (CF) for network intrusion detection. AIS is a powerful tool in terms of extirpating antigens inspired by the principles and processes of the natural immune system. PBIL uses past experiences to evolve into new species through learning and adopting the idea of CF for classification. The novelty of this research is in its combining of the three above mentioned approaches to develop a new classifier which can be applied to detect network intrusion, with incremental learning capability, by adapting the weight of key features. In addition, four mechanisms: creating a new antibody using PBIL, dynamic adjustment of feature weighting using clonal expansion, antibody hierarchy adjustment using mean affinity, as well as usage rates, are proposed to intensify AIS performance. As shown by the comparison carried out with other artificial intelligence and evolutionary computation approaches in network anomaly detection problems, our PBIL-AISCF classifier can achieve high accuracy for the benchmark problem.