报告题目：A Data-Driven Mobile Malware Detection Framework

报告人：Professor Qiben Yan

报告人单位：University of Nebraska-Lincoln

主持：李进 教授

时间：2017年6月1日（星期四）上午10:15-11:15

地点：行政西楼前座428会议室

报告人简介：

Qiben Yan is an Assistant Professor in Computer Science and Engineering Department in University of Nebraska-Lincoln. He received Ph.D. degree in Computer Science from Virginia Tech, and his M.S. and B.S. degree in Electrical Engineering from Fudan University. He was once a security researcher in a cyber security startup company called Shape Security, where he participated in building the first “botwall”. His research interests are to design secure network infrastructure to protect the modern networks under threats, by applying techniques such as machine learning, statistical methods, and time series analysis. He is particularly interested in mobile security, IoT security, wireless security and privacy..

报告摘要：

The security and privacy issues of mobile platform have aroused concerns from both industry and academia. The economical promise of mobile Internet can be easily undermined by “smart” malware and botnet. It is terrifying to imagine that the sensitive data stored on mobile devices could be leaked to adversaries through mobile Internet, or a wealth of compromised mobile devices could launch a denial of service attack to destruct mobile infrastructures. Moreover, with the growing sophistication of malware on the mobile system, malware authors resort to command and control (C&C) techniques to form botnet in order to organize the malware infrastructure. In this talk, we will illustrate a data-driven mobile malware behavior monitoring framework to automatically analyze traffic data generated by malware samples in a real Internet environment. Specifically, we capture the application network traffic from a large repository malware samples, and analyze the major compositions of the application traffic data. Finally, we will discuss our other research topics related to secure wireless, mobile communications. This talk will show the importance of data analytics in developing security mechanisms to counteract cyber threats.