LAUSR

Network traffic classification (NTC) is essential to network measurement, management, and security. Recently, Artificial Intelligence (AI)-based NTCs have demonstrated its accuracy in identifying data traffic. However, existing AI-based NTCs require inputs of lengthy data payload with padding, which can lead to complex designs of NTC models that are especially challenging to networking edge devices, e.g., Wi-Fi routers, etc. To address this issue, an adaptive and lightweight NTC framework is proposed in this paper. In specific, an input feature contribution extraction scheme is developed to weigh each input feature based on both the significance and the uniqueness of the corresponding feature. The optimal set of input features are determined to minimize the complexity of a targeting AI-based NTC while maintaining high performance in classification. Moreover, an autonomous update scheme is proposed to detect the changes in feature contribution and process updates. Evaluations on two fundamental AI-based NTC models, i.e., MLP and CNN, demonstrated that the proposed scheme can significantly reduce the input features and accelerate NTC models by one to two magnitudes while maintaining high accuracy. The proposed autonomous update scheme can accurately detect a change in feature contributions and update the NTC models to sustain the high accuracy.