时间：2013年9月12日(星期四)下午15:00
地点：仓山校区成功楼603室
主讲：Professor Yang Xiang (项阳)
School of Information Technology, Deakin University, Australia
主办：数学与计算机科学学院
专家简介：项阳教授的研究领域包括网络和系统安全，分布式系统安全等，作为项目负责人主持了多个澳大利亚政府ARC项目的研发；在各类学术期刊和学术会议上发表了130多篇学术论文；应邀担任了10多个国际学术会议的大会主席和50多个国际学术会议的程序委员会委员；目前担任《IEEE Transactions on Computers》、《IEEE Transactions on Parallel and Distributed Systems》、《Security and Communication Networks》和 《Journal of Network and Computer Applications》等SCI期刊的编委。
报告摘要：With the arrival of Big Data Era, properly utilizing the power of big data is becoming increasingly essential for the strength and competitiveness of businesses and organizations. We are facing grand challenges from big data from different perspectives, such as processing, communication, security, and privacy. In this talk, we discuss the big data challenges in network traffic classification and our solutions to the challenges. The significance of the research lies in the fact that each year the network traffic increase exponentially on the current Internet.
Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine-learning techniques to flow statistical feature based classification methods. In this talk, we propose a series of novel approaches for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approaches and their performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic datasets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples. Our work has significant impact on security applications.