Prof. Kin-Choong Yow, University of Regina, Canada

Kin-Choong Yow obtained his B.Eng (Elect) with 1st Class Honours from the National University of Singapore in 1993, and his Ph.D. from Cambridge University, UK in 1998. He joined the University of Regina in September 2018, where he is presently a Professor in the Faculty of Engineering and Applied Science. Prior to joining UofR, he was an Associate Professor in the Gwangju Institute of Science and Technology (GIST), Republic of Korea, (2013-2018), Professor at the Shenzhen Institutes of Advanced Technology (SIAT), P.R. China (2012-2013), and Associate Professor at the Nanyang Technological University (NTU), Singapore (1998-2013). In 1999-2005, he served as the Sub-Dean of Computer Engineering in NTU, and in 2006-2008, he served as the Associate Dean of Admissions in NTU.

Kin-Choong Yow’s research interest is in Artificial General Intelligence and Smart Environments. Artificial General Intelligence (AGI) is a higher form of Machine Intelligence (or Artificial Intelligence) where the intelligent agent (or machine) is able to successfully perform any intellectual task that a human being can. Kin-Choong Yow has published over 100 top quality international journal and conference papers, and he has served as reviewer for a number of premier journals and conferences, including the IEEE Wireless Communications and the IEEE Transactions on Education. He has been invited to give presentations at various scientific meetings and workshops, such as ACIRS, in 2018 and 2019; ICSPIC, in 2018; and ICATME, in 2021. He is the Editor-in-Chief of the Journal of Advances in Information Technology (JAIT), a Managing Editor of the International Journal of Information Technology (IntJIT), and a Guest Editor of MDPI Applied Sciences. He is also a member of APEGS and ACM, and a senior member with the IEEE.

Title: Network Intrusion Detection via Flow-to-Image Conversion and Deep Neural Network Classification

In recent years, computer networks have become an indispensable part of our life, and these networks are vulnerable to various types of network attacks, compromising the security of our data and the freedom of our communications. In this talk, we will first discuss the state-of-the-art approaches in network intrusion detection, and then we will discuss a new intrusion detection approach that uses image conversion from network data flow to produce an RGB image that can be classified using advanced deep learning models. We will show how we can use various feature extraction algorithms to identify important features in the data, and then use a windowing and overlapping mechanism to convert the varying input size to a standard size image for the classifier. We will then show how we can use deep neural network classifiers such as the AlexNet and the Vision Transfomer (ViT) classifier to classify the resulting image. Our experimental results show that this approach can achieve better classification rates than existing approaches.