Combinatorial intrusion detection model based on deep recurrent neural network and improved SMOTE algorithm - Binghao YAN; Guodong HAN (网络与信息安全学报)
Existing intrusion detection models generally only analyze the static characteristics of network intrusion actions,resulting in low detection rate and high false positive rate,and cannot effectively detect low-frequency attacks.Therefore,a novel combinatorial intrusion detection model (DRRS) based on deep recurrent neural network (DRNN) and region adaptive synthetic minority oversampling technique algorithm (RA-SMOTE) was proposed.Firstly,RA-SMOTE divided the low frequency attack samples into different regions adaptively and improved the number of low-frequency attack samples with different methods from the data level.Secondly,the multi-stage classification features were learned by using the level feedback units in DRNN,at the same time,the multi-layer network structure achieved the optimal non-linear fitting of the original data distribution.Finally,the intrusion detection was completed by trained DRRS.The empirical results show that compared with the traditional intrusion detection models,DRRS significantly improves the detection rate of low-frequency attacks and overall detection efficiency.Besides,DRRS has a certain detection rate for unknown new attacks.So DRRS model is effective and suitable for the actual network environment.