Prof. Mohsen Guizani, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), UAE

IEEE Fellow

Mohsen Guizani (Fellow, IEEE) received the B.S. (Hons.), M.S., and Ph.D. degrees in electrical and computer engineering from Syracuse University, Syracuse, NY, USA, in 1985, 1987, and 1990, respectively. He is currently a Professor in machine learning and an Associate Provost with the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates. Previously, he worked in different institutions in the USA. He is the author of 11 books and more than 1000 publications and several U.S. patents. His research interests include applied machine learning and artificial intelligence, smart cities, the Internet of Things (IoT), intelligent autonomous systems, and cybersecurity. He was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2019, 2020, 2021, and 2022. He has won several research awards, including the “2015 IEEE Communications Society Best Survey Paper Award,” the Best ComSoc Journal Paper Award in 2021, and five best paper awards from ICC and GLOBECOM Conferences. He was a recipient of the 2017 IEEE Communications Society Wireless Technical Committee (WTC) Recognition Award, the 2018 AdHoc Technical Committee Recognition Award, and the 2019 IEEE Communications and Information Security Technical Recognition (CISTC) Award. He served as the Editor-in-Chief for IEEE Network and is currently serving on the editorial boards of many IEEE Transactions and magazines. He was the Chair of the IEEE Communications Society Wireless Technical Committee and the TAOS Technical Committee. He served as the IEEE Computer Society Distinguished Speaker and is currently the IEEE ComSoc Distinguished Lecturer.

Prof. Pin-Han Ho, University of Waterloo, Canada

IEEE Fellow

Dr. Pin-Han Ho received his B.Sc. and M.Sc. degree from the Electrical Engineering department in National Taiwan University in 1993 and 1995, respectively, and Ph.D. degree from Queen’s University at Kingston at 2002. He is now an associate professor in the department of Electrical and Computer Engineering, University of Waterloo, Canada. Professor Pin-Han Ho is the author/co-author of more than 150 refereed technical papers, several book chapters, and the co-author of a book on optical networking and survivability. His current research interests cover a wide range of topics in broadband wired and wireless communication networks, including survivable network design, wireless Metropolitan Area Networks such as IEEE 802.16 networks, Fiber-Wireless (FIWI) network integration, and network security. He is the recipient of Distinguished Research Excellent Award in the ECE department of U of Waterloo, Early Researcher Award (Premier Research Excellence Award) in 2005, the Best Paper Award in SPECTS'02, ICC'05 Optical Networking Symposium, and ICC'07 Security and Wireless Communications symposium, and the Outstanding Paper Award in HPSR'02.

Prof. Ljiljana Trajkovic, Simon Fraser University, Canada

IEEE Life Fellow

Co-Chair of the IEEE/SMCS Brain-Machine Interface Systems Technical Committee

Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. Dr. Trajkovic served as IEEE Division X Delegate/Director, President of the IEEE Systems, Man, and Cybernetics Society, and President of the IEEE Circuits and Systems Society. She serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems. She was a Distinguished Lecturer of the IEEE Circuits and System Society and a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society. She is a Fellow of the IEEE.

Speech Title: Data Mining and Machine Learning for Analysis of Network Traffic

Abstract: Collection and analysis of data from deployed networks is essential for understanding modern communication networks. Data mining and statistical analysis of network data are often employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic while various machine learning techniques proved valuable for predicting anomalous traffic behavior. In described case studies, traffic traces collected from various deployed networks and the Internet are used to characterize and model network traffic, analyze Internet topologies, and classify network anomalies.