报告人：Qiben Yan, Assistant Professor, University of Nebraska-Lincoln, US
题目: A Data-Driven Mobile Malware and Botnet Detection Framework
Abstract: 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 malwares on the mobile system, malware authors resort to command and control (C&C) techniques to form botnet in order to organize the malware infrastructure. Advanced botnets adopt novel distributed infrastructure for more resilient C&C. In this talk, I will illustrate a malware traffic behavior monitoring scheme to capture 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, I will discuss my other research topics related to wireless, mobile communications and advanced botnet detection. This talk will show the importance of data analytics in developing security mechanisms to counteract cyber threats.
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 a M.S. and B.S. degree in Electrical Engineering from Fudan University. 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.