Abstract:
The rapid growth of the Internet and related technologies has led to individuals, organizations, and society collecting large amounts of data. However, these large amounts of data often lead to information overload which occurs when the amount of input (e.g. data) that a human is trying to process exceeds their cognitive capacities. In turn, this can lead to humans ignoring, overlooking, or misinterpreting crucial information. To address this, Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data. ML allows computers to learn without being explicitly programmed. Upon learning patterns from a training set of data, the computer can apply what it has learned to find these patterns in similar data. ML has become an extremely popular topic within development organizations that are looking to adopt a data-driven approach to improve their business by gaining useful information from the data they collect. In this talk, we focus on one area in which ML has shown great promise, namely network security. More specifically, we illustrate the effectiveness of ML-based frameworks in tackling three different network security related problems: DNS typo squatting, intrusion detection in autonomous/connected vehicles, and intrusion detection in networked systems. Additionally, we present future research opportunities on how to further improve such frameworks.
Biography:
Dr. Abdallah Moubayed received the Ph.D. in Electrical & Computer Engineering from the University of Western Ontario in August 2018, M.Sc. degree in Electrical Engineering from King Abdullah University of Science and Technology, Thuwal, Saudi Arabia in 2014, and B.E. degree in Electrical Engineering from the Lebanese American University, Beirut, Lebanon in 2012. Before joining KFUPM, he worked as an Assistant Professor at Arizona State University and Lebanese American University. He has more than 40 publications in top tier transaction journals and conferences. Additionally, he has chaired key sessions for IEEE GLOBECOM, IEEE ICC, and IEEE WCNC conferences. His research interests include machine learning & data analytics, performance & optimization modeling, computer network security, wireless communication, resource allocation, wireless network virtualization, and cloud computing. | |