Department of Computer System & Technology
Faculty of Computer Science and Information Technology
Universiti Malaya

From Hands-On to Hands-Off: The Revolutionary Shift to Automated Network Traffic Classification


Network traffic classification is crucial for implementing network management policies, particularly for security. However, the surge in Internet-of-Things and encrypted traffic poses serious scalability and privacy issues, undermining classifier efficacy and potentially leading to security breaches, service agreement failures, and financial losses. This keynote aims to provide foresight into the future of network traffic classification, discussing potential innovations, emerging trends, and the evolving challenges in network security that professionals and businesses might face. The presentation delves into the transformative journey of network traffic classification, transitioning from labour-intensive manual processes to sophisticated automation. The presentation traces the historical context, highlighting the challenges and limitations inherent in manual classification methods, including scalability issues, the requirement for expert knowledge, and the potential for human error. It then explores the paradigm shift towards automation, emphasizing the integration of advanced technologies such as machine learning, deep learning, and artificial intelligence. This presentation examines case studies and real-world applications, illustrating how automated network traffic classification has revolutionized cybersecurity, network management, and user experience. Furthermore, the presentation offers insights into the future of network traffic classification, speculating on emerging trends, potential innovations, and the evolving landscape of network security. In conclusion, this keynote presentation charts the significant shift from manual to automated network traffic classification, highlighting the crucial role of technologies like machine learning and artificial intelligence in overcoming the challenges of scalability and privacy.


Nor Badrul Anuar is an accomplished academic leader with a wealth of experience in various leadership positions. He currently serves as the Associate Vice Chancellor for Infrastructure and Information Services and acts as the Chief Information Officer at Universiti Malaya, where he leads various university services, including ICT, physical infrastructure, sports, and library services. As a senate member, he also chairs many ICT and infrastructure development committees. In addition, he leads the Centre of Research for Cyber Security & Network (CSNET).

Nor Badrul Anuar began his academic journey at Universiti Malaya, earning his Bachelor's and Master's degrees in Computer Science in 2001 and 2003, respectively. He obtained his PhD at the Centre for Information Security & Network Research, University of Plymouth, UK 2012. After joining Universiti Malaya as a tutor in 2001, he was promoted to lecturer in 2003, senior lecturer in 2008, and associate professor in 2017. In 2022, he was promoted to full professor at the Faculty of Computer Science and Information Technology at Universiti Malaya, Kuala Lumpur. In addition, he is also serving as a visiting professor at the Institute of Informatics and Computing in Energy (IICE), Universiti Tenaga Nasional, Malaysia.

He has supervised numerous post-doctoral fellows and research students, including eighteen PhD and six master's research students, in the computer network security and applications domain between 2012 and 2022. He has published extensively on computer security and applications, with his work being featured in academic journals both domestically and internationally. As of January 2023, his 152 published papers have attracted 7,670 citations and obtained an H-index of 41, making him a highly cited scholar. He is recognised as an expert in several subject categories, including computer science, engineering, science & technology - other topics, telecommunications, and information science & library science, by Web of Science. In 2022, Stanford University recognised him as one of the world's top 2% scientists. Currently, he serves on the editorial board of two journals, including the Journal of Network and Computer Applications (JNCA), which has an impact factor of 7.574. He has received numerous honours and recognitions throughout his illustrious career. He was honoured by Universiti Malaya for eight consecutive years, from 2012 to 2019, for his outstanding service. In 2015, he was recognised as an outstanding lecturer in science at Universiti Malaya and was awarded the university's highest honour, the Universiti Malaya Excellence Award (Anugerah Cemerlang Universiti Malaya).

His research interests are intrusion detection systems, high-speed networks, and management information systems. Specifically, he focuses on intrusion detection systems, intrusion response systems, security events and management, digital forensic and network security, switching, routing, IPv6, and multicast. His current research explores network traffic classification and its applications and the role of federated learning in enhancing network security.


Professor Jin-Song Dong

Deputy Head, Department of Computer Science,
National University of Singapore (NUS)

Probabilistic Model Checking for Sports Analytics: Decisions Beyond LLM


Sports analytics encompasses the utilization of data science, artificial intelligence (AI), psychology, and Internet of Things (IoT) devices to enhance sports performance, strategy, and decision-making. This process involves the processing and interpretation of cloud-based data from a variety of sources, such as video recordings and performance metrics. The resulting insights aid in evaluating player and team performance, preventing injuries, and supporting coaches and managers in making well-informed decisions and achieving superior outcomes. One widely recognized formal method, Probabilistic Model Checking (PMC), has traditionally been employed in reliability analysis for complex safety-critical systems. For instance, the reliability of an aircraft can be determined by evaluating the reliability of its components, including the engine, wings, and sensors. We apply PMC to Sports Analytics. As an example, the reliability (winning percentage) of a tennis player can be ascertained from the reliability (success rate) of their specific sub-skill sets (e.g., serve, forehand, backhand, etc). In this presentation, we will discuss our recent research work, which involves the application of PMC, machine learning, LLM, and computer vision to sports analytics. The reasoning capabilities of LLM will also be discussed. At the end of the presentation, we will present the vision of a new international sports analytics conference series https://formal-analysis.com/isace/2024/


Jin Song Dong is a professor and deputy head of the Computer Science Department at the National University of Singapore. His research interests include safety and security systems, sports analytics, and trusted machine learning/LLM reasoning. He co-founded the commercialized PAT verification system which has garnered thousands of registered users from over 150 countries.

Jin Song co-founded the commercialized trusted machine learning system Silas (www.depintel.com). He has received numerous best paper awards and served on the editorial board of ACM Transactions on Software Engineering and Methodology and Formal Aspects of Computing. He has successfully supervised 30 PhD students and is an Institute of Engineers Australia Fellow.

In his leisure time, Jin Song developed Markov Decision Process models for tennis analysis using PAT, assisting professional players with pre-match analysis (beating the world's best). He is a Junior Grand Slam coach and coached tennis to his three children, all of whom have reached the #1 national junior ranking in Singapore/Australia.

Two of his children have earned NCAA Division 1 full scholarships, while his second son, Chen, played #1 singles for Australia in the Junior Davis Cup Final and participated in both the Australian Open and US Open Junior Grand Slams.

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