报告人：罗自炎，教授，北京交通大学

地点：腾讯会议（ID：882 713 363）

时间：2021年3月16日 8:30-11:30

摘要：Internet traffic anomaly detection (ITAD) is a critical task for various network tasks such as traffic engineering and network security. Traditional matrix-based approaches of ITAD have limitations for traffic data with multi-way structures, while the emerging tensor-based approaches of ITAD lack of sufficient consideration for circumstances including incomplete measurements or link-load measurements. To address these issues, we formulate ITAD by a sparse low-rank tensor optimization model, taking into full consideration the intrinsic and potential properties including the sparsity of anomalies, the low-rankness and temporal stability and periodicity of the normal traffic data. Although the resulting optimization model is non-convex and discontinuous due to the involved L0-norm and the tensor rank function, optimality analysis via stationarity is established, based on which an efficient proximal gradient method with theoretical convergence to stationary points is designed. Numerical experiments on Internet traffic trace data Abilene and GEANT demonstrate the high efficiency of our proposed sparse and low-rank tensor-based approach (SLRTA) for ITAD.

报告人简介

罗自炎，女，北京交通大学理学院教授、博士生导师。2010年获北京交通大学理学院运筹学与控制论专业博士学位，美国Stanford大学管理与科学工程系、新加坡国立大学、英国南安普顿大学访问学者、香港理工大学应用数学系研究助理。主要从事大规模统计优化算法设计、稀疏与低秩优化、张量分析与张量理论等方面的研究。共发表SCI检索期刊论文30余篇，其中ESI高被引论文2篇。撰写英文专著1部，由国际著名SIAM出版社于2017年4月出版，编写中文著作《半定规划》, 已被国内多所高校的优化专业选为研究生教材。主持国家自然科学基金面上项目、国家自然科学基金重点项目子课题、国家自然科学基金青年基金项目、北京市自然科学基金重点项目各1项。2016年在北京运筹学年会上做大会特邀报告，2017年在第十一届全国数学规划学术会议上做青年专题报告，2020年获中国运筹学会青年科技奖提名奖。