Ensemble Intrusion Detection Based on Heterogeneous Data Augmentation and Knowledge Distillation
Longhui Wang, Xu Zhou, Weiping Ding, Lifang Chen, Qi Dai · IEEE Transactions on Industrial Informatics · 2025
The number and complexity of network attacks and intrusion events are constantly increasing, timely detection of abnormal intrusion behavior is an important challenge in the field of network security. To this end, this article proposes an ensembled intrusion detection model based on heterogeneous data augmentation and mutual knowledge distillation (KD), KDEHDA. To enhance the diversity of data, the training set is divided into multiple data subsets using the bootstrap sampling method, and different data augmentation methods are used on each subset to obtain multiple balanced data subsets. To enhance the generalization ability of the model, different base CNN models are trained using balanced data subsets, and KD is used to transfer the weight information of CNN. The final result is the voting ensemble of each CNN model. Experimental results show that the proposed model is superior to the existing optimal model.