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CS Seminar| Machine Learning Practices in Security Data Analysis

Start Date: March 21, 2017
End Date: March 21, 2017

By Dr. Yufei Han (Symantec Research Labs)

Nowadays Security vendors face more and more challenges in processing automatically seas of security incidents. Introducing artificial intelligence at different levels becomes key to business success of these companies, leading to more active and more efficient defensive strategy and actions against cyber-attacks. In this talk, several practical use case of security data analysis will be presented. The aim is to explain the challenging issues of industrial data analytical workflows and discuss how to combine theoretical machine learning research output and applied data mining tasks.

Biography: Yufei has extensive background in various machine learning techniques, including semi-supervised classification, clustering, and probabilistic graph models for Bayesian inference. He obtained his Phd degree at National Laboratory of Pattern Recognition, Chinese Academy of Sciences in 2010. He worked as research scientist at INRIA (France) from 2010 to 2013. During that slot, he conducted applied machine learning research in transportation (traffic modeling) and fault diagnosis for mechanical defects of transportation systems. He is currently working as principal researcher at Symantec Research Labs. His research focus is to automate security response and actively prevent potential security risks using machine learning techniques. Most recently, he took part in the BigFoot project (FP7), funded by the European Community's Seventh Framework Programme (FP7). His work in this project included developing time series forecasting for semi-supervised feature selection for security applications of Symantec.

More Information:

For more info contact: Xiangliang Zhang: email: xiangliang.zhang@kaust.edu.sa

Date: Tuesday 21st Mar 2017
Time:12:00 PM - 02:00 PM
Location: Building 1, Rm. 3422,
Refreshments: Light lunch will be provided at 11:45am