WF-PST Tutorial Program


Tutorial 1: Assessing Cybersecurity of Public Safety Infrastructure

Presenter: Dr. Filippo Berto
Others contributors: Marco Anisetti, Claudio A. Ardagna

ABSTRACT: Nowadays, sensors are deployed in public areas for instance with the scope of monitoring people’s behaviors for safety reasons. Such sensors (e.g., video cameras and air pollution sensors) gather data to be processed by services hosted on a distributed ICT infrastructure. Safety-related services aim to address specific goal such as criminal behavior detection, critical massive people movement prediction, critical pollution concentration detection, and traffic anomalies prediction. They have to be deployed in the ICT infrastructure in order to satisfy specific QoS requirements. Such infrastructure is therefore becoming a crucial asset to guarantee public safety, and its cybersecurity is of paramount importance. A simple denial of service at the infrastructure level, for instance, can have a tremendous impact on the ability to detect safety-critical event. Guarantee cybersecurity of critical public safety distributed ICT infrastructure poses a number of challenges including the difficulties of verifying weaknesses and vulnerabilities in capillary distributed and hybrid architecture. The trend of using 5G and Telco networks to connect sensors and cover areas where wired connections are not available is even making the scenario more complex. In this tutorial, Moon Cloud, a cybersecurity monitoring framework is presented. It provides a framework for governing cybersecurity in a distributed architecture offering both a holistic view and details on how to address the detected cybersecurity issues. In addition, the tutorial will also present MUSA a Framework for deploying services on distributed ICT infrastructure making them monitorable for security and privacy.

Filippo BertoBIO: Filippo Berto is a Research Fellow at Università degli Studi di Milano, Italy. His research interests are in the areas of cybersecurity, edge computing, and distributed systems. In particular, he works in the field of Security Assurance, 5G networks, and Edge-Cloud Computing, focusing on networks and services certification techniques. He’s a member of Sesar Lab and a TIM S.p.A. sponsored Ph.D. student, through the UniversiTIM project.

Tutorial 2: On AutoML for Intrusion Detection in 6G and IoT Systems: From Theory to Practice

Presenters: Dr. Abdallah Shami and Dr. Li Yang

ABSTRACT: This tutorial presents an in-depth exploration of Automated Machine Learning (AutoML) in enhancing cybersecurity for the upcoming 6G networks and Internet of Things (IoT) systems, with a specific focus on Intrusion Detection Systems (IDS). The transition to 6G brings forth intricate security challenges in the rapidly expanding IoT landscape, necessitating advanced and effective solutions. The tutorial aims to bridge the gap between current network security challenges and the innovative solutions AutoML provides. Beginning with an overview of 6G and IoT cybersecurity, the tutorial emphasizes the need for robust IDS to combat sophisticated cyber-attacks and explores IDS enhancement through AI and ML. The tutorial discussed the limitations of traditional ML models in network security, such as human intervention requirements and model drift in dynamic IoT environments. The key part of the tutorial is the discussion on AutoML techniques, which automate critical ML processes, including data pre-processing, feature engineering, model selection, hyperparameter optimization, and model updating. These methods offer adaptive, autonomous cybersecurity solutions, overcoming traditional ML model constraints. A practical case study on public benchmark cybersecurity datasets is included to illustrate AutoML’s application in IDS development. Aimed at a broad audience of researchers, industry professionals, and students, the tutorial is essential for those involved in cybersecurity, machine learning, and telecommunications. It provides insights into the latest developments in cybersecurity and the transformative potential of AutoML in IDS for 6G and IoT.

Abdallah ShamiBIO: Abdallah Shami is a professor with the ECE Department at Western University, Ontario, Canada. He is the Director of the Optimized Computing and Communications Laboratory at Western University ( He is currently an associate editor for IEEE Transactions on Mobile Computing, IEEE Network, and IEEE Communications Surveys and Tutorials. He has chaired key symposia for IEEE GLOBECOM, IEEE ICC, IEEE ICNC, and ICCIT. He was the elected Chair of the IEEE Communications Society Technical Committee on Communications Software (2016–2017) and the IEEE London Ontario Section Chair (2016–2018).


Li YangBIO: Li Yang received the B.E. degree in computer science from Wuhan University of Science and Technology, Wuhan, China in 2016 and the MASc degree in Engineering from University of Guelph, Guelph, Canada, 2018. Dr. Li Yang is currently a Tenure-Track Assistant Professor in the Faculty of Business and Information Technology at Ontario Tech University, Oshawa, ON, Canada. He has been working toward the Ph.D. degree in the Department of Electrical and Computer Engineering, Western University, London, Canada. His research interests include cybersecurity, machine learning, data analytics, and intelligent transportation systems.