Zero-Day Traffic Identification

摘要：

　　Recent research on Internet traffic classification has achieved certain success in the application of machine learning techniques into flow statistics based method.However, existing methods fail to deal with zeroday traffic which are generated by previously unknown applications in a traffic classification system.To tackle this critical problem, we propose a novel traffic classification scheme which has the capability of identifying zero-day traffic as well as accurately classifying the traffic generated by pre-defined application classes.In addition, the proposed scheme pro vides a new mechanism to achieve fine-grained classification of zero-day traffic through manually labeling very few traffic flows.The preliminary empirical study on a big traffic data show that the proposed scheme can address the problem of zero-day traffic effectively.When zero-day traffic present, the classification performance of the proposed scheme is significantly better than three state-of-the-art methods, random forest classifier, classification with flow correlation, and semi-supervised traffic classification.

关键词： Traffic classification semi-supervised learning zero-day applications

作者: Jun Zhang Xiao Chen Yang Xiang Wanlei Zhou

作者单位: School of Information Technology,Deakin University,Melbourne,Australia,3125

会议类型: 国际会议

会议名称: The 5th International Symposium on Cyberspace Safety and Security ( CSS2013)(第五届国际网络空间安全和安保研讨会)

会议地点: 张家界

会议语种:英文

页码: 213-227

在线出版日期: 2013-11-13（万方平台首次上网日期，不代表论文的发表时间）