Robust AI: Adversarial training and time series AI

Step into the world of resilient AI models! Our thesis tackles the growing threat of cyber-attacks on Artificial Intelligence systems, particularly in the realm of time-series AI. As AI becomes integral to critical processes, we explore how cyber threats can target prediction and decision-making, risking privacy, accessibility, and integrity. This project conducts a detailed analysis of vulnerabilities and threats, studies the impact on specific applications, and proposes practical defenses. Join us in enhancing AI resilience, employing techniques like adversarial training to fortify against evolving cyber challenges. It's a journey into securing the future of smart technology!

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 adversarial training methods on an industrial control system dataset, in order to increase resilience of time series AI. First experiments should be done using publically available datasets and state-of-the-art time series methods, while later on more challenging data readings will be provided in the project.

  • Thorough literature research on the topic;
  • Select suitable time series methods;
  • Select suitable adversarial training methods;
  • Design and conduct experiments to investigate the applicability of adversarial training 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.