Intrusion Detection Algorithm Based on Random Forest and Artificial Immunity
摘要
Traditional intrusion detection methods have low detection rates for Probe,U2R,R2L and other types of network intrusion attacks,leading to misdetection and missed detection of intrusion behavior.Therefore,this paper proposes an intrusion detection algorithm based on Random Forest(RF) and artificial immunity.A Random Antibody(RF) forest detection strategy is designed,and a Clone Selection Algorithm(CSA) for small sample datasets is adopted to ensure the superiority of antibodies and improve the detection rate of attacks.Then the antigen recognized as intrusive behavior is injected into the antibody set to balance the detection rate and false alarm rate of the antigen.Simulation results show that the detection rate of the proposed algorithm is 94.1%,which is higher than that of Probe (93.79%),U2R (91%) and R2L (85%).The proposed algorithm also has a low false alarm rate.