Research on Lightweight Deep Learning Intrusion Detection in Edge Computing Environments
This research aims to develop a lightweight deep learning model for network intrusion detection in edge computing environment.First,analyze and select a lightweight model architecture suitable for edge computing,and combine model compression and optimization technologies,such as weight pruning,quantification and knowledge distillation,to reduce the computational complexity of the model.Secondly,the designed model is verified in an actual edge computing environment.The experimental results show that the model significantly reduces the consumption of computing resources while maintaining high detection accuracy.
lightweight deep learning modelsedge computingnetwork intrusion detectionmodel compres-sionknowledge distillation
万方数据
维普
