Keynote Speaker

Title: The Evolution of System Audit Logging: From Post-Incident Forensics to Real-Time Causal Detection and the Future of Mobile Security

Bio:

Dr. Kyu Hyung Lee is an Associate Professor and the Director of Graduate Studies in the School of Computing at the University of Georgia, where he also serves as the Interim Director of the Institute for Cybersecurity and Privacy. He received his PhD in Computer Science from Purdue University in 2014.
 
Dr. Lee is a leading expert in software security and cyber forensics, specializing in system audit logging, causal graph analysis, and enterprise-scale forensic investigations. His research leverages program analysis to solve critical security challenges and automate post-incident threat tracking. His work has earned several honors, including the USENIX Security Distinguished Paper Award. He currently serves on the organizing committee of NDSS, and frequently contributes to the program committees of top-tier security venues, including USENIX Security, ACM CCS, IEEE S&P, and NDSS.
 
See more:

https://kyuhlee.github.io/ 

This keynote talk provides a comprehensive vision of the evolution of system audit logging, tracing its transformation from a retrospective forensic tool into an AI-driven, real-time defense system, and explores its critical implications for the mobile future. Traditionally serving as a high-fidelity audit trail to investigate sophisticated cyber attacks, audit logging initially focused on high-speed ingestion for passive, post-incident analysis. As computing scaled, the resulting log deluge shifted the academic focus toward causality-preserving log reduction to make post-mortem attack analysis scalable but still retrospective.
 
More recently, the convergence of provenance graphs and Graph AI (GNN) has fundamentally evolved logging into an automated, real-time intrusion detection system capable of identifying complex attacks based on deep causal relations. However, as we transition to highly interconnected mobile and edge environments, this real-time defense faces a critical threat from kernel compromise, which directly undermines the integrity of the audit logging mechanism itself. This talk concludes by examining the next frontier of establishing zero-trust logging paradigms under untrusted environments, discussing open architectural challenges and potential paths forward to protect log integrity within the stringent resource constraints of next-generation mobile systems.
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Nicos Maglaveras

Professor of Medical Informatics Aristotle University of Thessaloniki Greece

Personalised health driven by digital health systems and multi-source health/environmental data, ML/AI/DL analytics and predictive models

Nicos Maglaveras received the diploma in electrical engineering from the Aristotle University of Thessaloniki (A.U.Th.), Greece, in 1982, and the M.Sc. and Ph.D. degrees in electrical engineering with an emphasis in biomedical engineering from Northwestern University, Evanston, IL, in 1985 and 1988, respectively. He is currently a Professor of Medical Informatics, A.U.Th. He served as head of the graduate program in medical informatics at A.U.Th, as Visiting Professor at Northwestern University Dept of EECS (2016-2019), and is a collaborating researcher with the Center of Research and Technology Hellas, and the National Hellenic Research Foundation.

His current research interests include biomedical engineering, biomedical informatics, ehealth, AAL, personalised health, biosignal analysis, medical imaging, and neurosciences. He has published more than 500 papers in peer-reviewed international journals, books and conference proceedings out of which over 160 as full peer review papers in indexed international journals. He has developed graduate and undergraduate courses in the areas of (bio)medical informatics, biomedical signal processing, personal health systems, physiology and biological systems simulation.

He has served as a Reviewer in CEC AIM, ICT and DGRT D-HEALTH technical reviews and as reviewer, associate editor and editorial board member in more than 20 international journals, and participated as Coordinator or Core Partner in over 45 national and EU and US funded competitive research projects attracting more than 16 MEUROs in funding. He has served as president of the EAMBES in 2008-2010. Dr. Maglaveras has been a member of the IEEE, AMIA, the Greek Technical Chamber, the New York Academy of Sciences, the CEN/TC251, Eta Kappa Nu and an EAMBES Fellow.

The last years saw a steep increase in the number of wearable sensors and systems, mhealth and uhealth apps both in the clinical settings and in everyday life. Further large amounts of data both in the clinical settings (imaging, biochemical, medication, electronic health records, -omics), in the community (behavioral, social media, mental state, genetic tests, wearable driven bio-parameters and biosignals) as well as environmental stressors and data (air quality, water pollution etc.) have been produced, and made available to the scientific and medical community, powering the new AI/DL/ML based analytics for the identification of new digital biomarkers leading to new diagnostic pathways, updated clinical and treatment guidelines, and a better and more intuitive interaction medium between the citizen and the health care system.

Thus, the concept of connected and translational health has started evolving steadily, connecting pervasive health systems, using new predictive models, new approaches in biological systems modeling and simulation, as well as fusing data and information from different pipelines for more efficient diagnosis and disease management.

In this talk, we will present the current state-of-the-art in personalized health care by presenting cases from COVID-19 and COPD patients using advanced wearable vests and new technology sensors including lung sound and EIT, new outcome prediction models in COVID-19 ICU patients fusing X-Rays, lung sounds, and ICU parameters transformed via AI/ML/DL pipelines, new approaches fusing environmental stressors with -omics analytics for chronic disease management, and finally new ML/AI-driven methodologies for predicting mental health diseases including suicidality, anxiety, and depression.

 
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