Embark on a journey into the world of cutting-edge security for Industrial Control Systems (ICS) and Edge-to-Cloud technology with our BSc/MSc thesis project! We explore the exciting world of anomaly detection, time series analysis, and artificial intelligence to strengthen industrial operations against cyber threats. Our focus is on using advanced techniques like federated learning (FL) to build a strong defense that covers everything from the edge of the system to the cloud. This unique approach not only improves early detection of cyber attacks but also prioritizes the protection of privacy. Join us in uncovering the secrets of cyber resilience in industrial settings, where each discovery brings us closer to a safer and more connected future.
The goal of this work is to apply state-of-the-art federated learning methods on an ICS dataset, in order to detect anomalies originating from cyber intruders. First experiments should be done using publically available datasets and both centralised anomaly detection and federated learning, while later on more challenging data readings will be provided in the project, including different types of data.