A dual-module intrusion detection model using GAN oversampling plus cost-sensitive attention CNN achieves 84.55% five-class and 91.09% binary accuracy on NSL-KDD.
CSE-IDS: Using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems
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CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data
A dual-module intrusion detection model using GAN oversampling plus cost-sensitive attention CNN achieves 84.55% five-class and 91.09% binary accuracy on NSL-KDD.