Catching the intrusion before the damage.
When an attacker reaches through a digital channel into a physical process, the signal shifts before anything breaks. This workshop is about detecting that shift — at the scale of real cyber-physical systems, with big data analytics, machine learning and signal processing.
Cyber-physical systems now run the grid, the water, the factory floor and the operating room. The same connectivity that makes them efficient gives adversaries a path from a network packet to a physical consequence.
Conventional intrusion detection was built for IT — not for the timing constraints, proprietary protocols and safety-critical physics of operational technology. Meanwhile the data keeps growing: time-series sensor readings, industrial network traffic, operational logs and actuator states arriving in volume, velocity and variety that only big-data pipelines can absorb.
Deep learning has proven it can learn the behavioral fingerprints hidden in that data. BDACPS 2026 brings together the data-engineering, signal-processing, industrial-IoT and security communities to push detection from the lab into live production environments — and to confront the hard parts head-on.
Hosted under IEEE Big Data 2026's focus on scalable data infrastructure and intelligent analysis, this workshop extends that agenda into operational-technology security, building on the team's prior SPID-CPS @ IEEE ICASSP 2024 satellite event.
We welcome original work — theoretical foundations and applied systems alike — across six tracks. The list is indicative, not exhaustive.
All deadlines are 23:59 AoE. Workshop deadlines are tentative and will be aligned with the IEEE Big Data 2026 workshop schedule.
Papers are peer-reviewed and accepted contributions appear in the IEEE Big Data 2026 proceedings, indexed in IEEE Xplore.
15–20 minutes including Q&A, in a single full-day track.
Distinguished voices from academia and industry on CPS security at scale.
A moderated discussion on the unsolved problems in big-data-driven CPS defense.
An interactive showcase and networking session, subject to submission volume.
Assistant Professor working on deep learning and big data analytics, with a focus on AI for Industry 4.0. PhD in Technology, Innovation & Management; co-organizer of SPID-CPS @ IEEE ICASSP 2024.
Tenure-track Assistant Professor in time-series forecasting, data-driven AI and explainable AI applied to CPS and healthcare. TPC member for IJCAI 2026 and guest editor for several international journals.
Full Professor of Database and Information Systems, author of 100+ journal and conference publications. Research spans multimedia, knowledge management and big data analytics.