DDoS Attack Detection Using Big Data and Machine Learning

Speaker:     Prof. Jie Li

Time:        11:00-12:00, Dec. 26

Location:    SIST 1C-502

Host:          Prof. Yang Yang

Abstract:

The DDoS (Distributed Denial of Service) is a typical cyber-attack that makes network resources unavailable by overwhelming it with large traffic from multiple sources. In a typical DDoS attack, there may be millions of fake requests which run out the network resources. Nowadays, the scalability and traffic of networks grow explosively, which make the DDoS attack detection more complicated and challenging. Big data and machine learning can be powerful tools for dealing with DDoS detections by catching the network traffic features. In this speech, we present some novel DDoS detection and countermeasure methods for large-scale and complicated computer networks including Software-Defined Networks and large-scale Internet using big data framework Spark Streaming and machine learning. We show that the big data and machine learning are powerful in dealing with DDoS.

Bio: 

Jie Li is a Chair Professor in Department of Computer Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University. His research interests are in big data, computer networking, network security, modeling and performance evaluation of information systems. He is a co-chair of IEEE Big Data Community and chair of IEEE ComSoc Technical Committee on Big Data. He received the B.E. degree in computer science from Zhejiang University, Hangzhou, China, the M.E. degree in electronic engineering and communication systems from China Academy of Posts and Telecommunications, Beijing, China. He received the Dr. Eng. degree from the University of Electro-Communications, Tokyo, Japan. He was a full professor in University of Tsukuba, Japan. He is a senior member of IEEE and ACM. He has served on editorial boards of IEEE journals and transactions.