I know I don't know:an evidential deep learning framework for traffic classification  

作　　者：Shangsen LI Lailong LUO Yun ZHOU Deke GUO Xiang XU 

机构地区：[1]National Key Laboratory of Information Systems Engineering,National University of Defense Technology,Changsha 410000,China

出　　处：《Frontiers of Computer Science》2024年第5期219-221,共3页计算机科学前沿（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(Grant Nos.62302510 and U23B2004);the Changsha Science and Technology Bureau(KQ2009009);the Huxiang Youth Talent Support Program(2021RC3076).

摘　　要：Traffic classification is a crucial task for network security.One of the most difficult challenges is to accurately identify the traffic of unknown applications as well as discriminate the known classes.The current learning-based classifiers can achieve high classification accuracy for the known traffic[1,2],but are infeasible toclassifyeither the unknown/unseen/unlabeled application or zero-day application traffic[3],which is known as identification of unknown applications(IUA).Although the clustering-based methods can identify the unknown traffic,they need lots of human intervention to thefeaturechoice,hyperparameter configuration and known/unknown traffic division[4].

关 键 词：NETWORK TRAFFIC classify 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]