电子科技大学学报Issue(2)：241-246,6.DOI:10.3969/j.issn.1001-0548.2014.02.016
基于RVM的网络流量分类研究
Network Traffic Classification Based on Relevant Vector Machine
摘要
Abstract
Relevant vector machine (RVM) is applied in network traffic classification. Firstly, experiment data is standardized, and then RVM is compared with other machine learning tools. Lastly, doubting interval is introduced to analyze predicted probability of classification, based on which a new hybrid traffic classification approach is proposed. Experiment studies illustrate that:1) RVM excels the support vector machine (SVM) in three performances, and moreover, its classification accuracy is rather high in the situation of small sample circumstances;2) probabilistic classification in doubting interval has a rather low classification accuracy while an accuracy above 98%outside doubting interval.关键词
置疑区间/机器学习/相关向量机/流量分类Key words
douting interval/machine learning/relevant vector machine/traffic classification分类
信息技术与安全科学引用本文复制引用
柏骏,夏靖波,鹿传国,李明辉,任高明..基于RVM的网络流量分类研究[J].电子科技大学学报,2014,(2):241-246,6.基金项目
陕西省科技计划自然基金重点项目(2012JZ8005)；全军军事学研究生课题(2010XXXX-488) （2012JZ8005）
