计算机工程2026，Vol.52Issue(8)：138-148,11.DOI:10.19678/j.issn.1000-3428.0252067
空间优化视角下基于增量学习的网络流量分类
Network Traffic Classification Based on Incremental Learning from the Perspective of Space Optimization
摘要
Abstract
With the continuous evolution of network environments and the constant emergence of Internet applications,the applicability of machine learning classifiers trained on previous traffic data to new sample spaces gradually weakens.This leads to a decline in the recognition capability of classification models;thus,these models are ineffective in meeting the ever-increasing demands of network services and security.If the classifier is manually updated based on experience,it consumes a significant amount of effort,and it is difficult to ensure the generalization performance of the new classifier.Moreover,the continuous influx of new data poses severe challenges in balancing model training accuracy with the storage of computational resources.To address this issue,an incremental learning strategy employing spatial optimization techniques is proposed to achieve efficient network traffic classification.First,by optimizing the spatial distribution of old and new traffic samples,clusters of different categories(both old and new)are maintained with minimum intervals to avoid distribution conflicts arising from old and new tasks shared feature space.Within the optimized feature space,a small number of old data samples are replayed,and knowledge distillation techniques were utilized to maintain the stability of the original model parameters.Only the extended parts of the model are adjusted to update the classifier at minimal cost.Experimental results on the USTC-TFC2016 dataset demonstrate that,compared with other methods,this approach exhibits higher stability and effectiveness in terms of model accuracy,resource consumption,and performance.关键词
网络流量/增量学习/知识蒸馏/灾难性遗忘/深度学习Key words
network traffic/incremental learning/knowledge distillation/catastrophic forgetting/deep learning分类
信息技术与安全科学引用本文复制引用
胡财富,魏波,任芮彬..空间优化视角下基于增量学习的网络流量分类[J].计算机工程,2026,52(8):138-148,11.基金项目
国家自然科学基金青年科学基金项目(12102369). （12102369）
