Intrusion detection models often face the problem of data imbalance during training,that is,the number of samples of normal behavior far exceeds the number of samples of abnormal intrusion behavior.In order to solve the problem of data imbalance,the deep forest and LightGBM are combined as an intrusion detection model,in which richer features are generated by multi-granulari-ty scanning in the deep forest as the input of LightGBM,so as to improve the performance of the classifier.Moreover,the feature representation generated by deep forest can improve the distinguishability of minority samples,and with the weight adjustment mecha-nism of LightGBM,it can better deal with unbalanced data problems,and the brown bear optimization algorithm with powerful global search ability is used to tune the parameters of the model to further improve the prediction accuracy of the model.The proposed meth od is verified on the UNSW_NB15 dataset,and the BOA-DF-LightGBM model is better than other model indicators,with the predic-tion accuracy reaching 95.15％,which is nearly 2％higher than DF.In order to further verify its ability to solve the problem of data imbalance,the accuracy of the BOA-DF-LightGBM model in the data imbalance experiment is 94.23％,which is 2.68％higher than that of DF and 3.42％higher than that of neural network model.The effectiveness and superiority of BOA-DF-LightGBM in the case of data imbalance are verified.
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