CAJ | 학술논문

针对当前业务流量的分类方式过于简略、识别结果不够确切的问题，提出基于状态特征的分类识别方法以精确识别流量数据中的用户行为。定义了网络通信中的用户行为并分析其特征，通过矢量量化技术结合主题模型方法从流量序列中提取行为状态特征，利用机器学习算法对状态特征建模，并按照用户行为的分类对流量进行识别。实验结果显示按照行为分类能更加详细地描述流量特点。在相同机器学习算法下，基于状态特征的行为识别方法准确度优于传统方法。
침대당전업무류량적분류방식과우간략、식별결과불구학절적문제，제출기우상태특정적분류식별방법이정학식별류량수거중적용호행위。정의료망락통신중적용호행위병분석기특정，통과시량양화기술결합주제모형방법종류량서렬중제취행위상태특정，이용궤기학습산법대상태특정건모，병안조용호행위적분류대류량진행식별。실험결과현시안조행위분류능경가상세지묘술류량특점。재상동궤기학습산법하，기우상태특정적행위식별방법준학도우우전통방법。
General traffic classification methodologies cannot describe the detail information of network communication.This paper proposed a novel method based on users’online activities to classify traffic.This method described different traffic with characteristics of activity status,and combined vector quantization with topic model to extract characteristics of activity status from traffic.Then it built classification model and identified traffic with these characteristics by some machine learning algo-rithms.The experimental results show that category of users’activities give a more elaborate scheme to traffic classification. And with the same machine learning algorithm,the recognition rate of classification based on characteristics of activity status is better than traditional ways’rate.