Distributed security: AI-Powered Cyber Attack Detection

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.

Student Target Groups:

  • Students of ICE/Telematics;
  • Students of Computer Science;
  • Students of Software Engineering.

Thesis Type:

  • Bachelor Thesis / Master Project.

Goal and Tasks:

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.

  • Thorough literature research on the topic;
  • Select suitable anomaly detection methods;
  • Select suitable federated learning method;
  • Design and conduct experiments to investigate the applicability of FL in such an environment;
  • Summarize the results in a written report, and prepare an oral presentation.

Recommended Prior Knowledge:

  • Programming skills in Python;
  • Prior experience with deep learning frameworks is desirable (preferably PyTorch);
  • Interest in the topic.


  • a.s.a.p.