当前位置: X-MOL 学术Comput. Commun. › 论文详情
Mobile network traffic pattern classification with incomplete a priori information
Computer Communications ( IF 2.816 ) Pub Date : 2020-11-21 , DOI: 10.1016/j.comcom.2020.11.003
Zhiping Jin; Zhibiao Liang; Yu Wang; Weizhi Meng

In complex networks systems like mobile edge infrastructures, real-time traffic classification according to application types is an enabling technique for network resource optimization and advanced security management. State-of-the-art schemes take advantage of machine learning techniques to train classification models based on behavioral characteristics of network traffic flows. Nonetheless, most existing studies assume complete a priori information of the application classes and formulate the task as a standalone multi-class classification problem. Such classification models cannot properly handle the unknown applications that are absent from the training set during the time of training. In this work, we propose a practical mobile network traffic classification scheme that builds robust classifiers based on incomplete a priori information. Specifically, the core idea is to extract the unknown patterns emerging in the network periodically to complement the initial labeled data set that only consists of a limited number of known applications. We propose two algorithms for the unknown pattern extraction step. One is based on iterative asymmetric binary classification and the other is based on constrained clustering. Empirical results based on a public data set show that the proposed scheme can effetively detect both known and unknown applications.

更新日期:2020-11-21
全部期刊列表>>
ERIS期刊投稿
欢迎阅读创刊号
自然职场,为您触达千万科研人才
spring&清华大学出版社
城市可持续发展前沿研究专辑
Springer 纳米技术权威期刊征稿
全球视野覆盖
施普林格·自然新
chemistry
物理学研究前沿热点精选期刊推荐
自然职位线上招聘会
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
ACS Publications填问卷
阿拉丁试剂right
苏州大学
林亮
南方科技大学
朱守非
内蒙古大学
杨小会
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
上海纽约大学
浙江大学
廖矿标
天合科研
x-mol收录
试剂库存
down
wechat
bug