A Single-Level Immune Learning Algorithm for Anomaly Detection

Anomaly detection is one of the main issues in ensuring computer security.Various artificial immune system(AIS) algorithms,from negative selection algorithm (NSA) to multilevel immune learning algorithm(MILA),are therefore developed to serve this purpose.Based on a critical study of the MILA approach,this paper proposes a single-level immune learning algorithm(SILA),which extends the ideas of MILA and pays more attentions to the problem space.The proposed algorithm contributes mainly to(improving) the effectiveness and efficiency of detector training,which is of great concern in all artificial(immune) systems.