Tobias Hürten
Orcid: 0009-0005-2121-0810
According to our database1,
Tobias Hürten authored at least 14 papers
between 2022 and 2026.
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Collaborative distances:
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Bibliography
2026
FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems.
Proceedings of the 2026 ACM Sustainability Week, 2026
2025
Design and evaluation of an Autonomous Cyber Defence agent using DRL and an augmented LLM.
Comput. Networks, 2025
Proceedings of the NOMS 2025 IEEE Network Operations and Management Symposium, 2025
Quantifying the Impact of Poisoning Attacks in Deep Neural Networks for Attack Classification.
Proceedings of the IEEE Military Communications Conference, 2025
Formal Verification of Autonomous Cyber Defense for Abstraction of Critical Network Scenarios.
Proceedings of the IEEE Military Communications Conference, 2025
Automating Cybersecurity Risk Assessment for AI Systems in Military & Critical Infrastructure.
Proceedings of the IEEE Military Communications Conference, 2025
Proceedings of the International Conference on Military Communication and Information Systems, 2025
Automating Cyber Threat Intelligence and Attack Chain Generation using Cyber Security Knowledge Graphs and Large Language Models.
Proceedings of the International Conference on Military Communication and Information Systems, 2025
2024
Proceedings of the IEEE Military Communications Conference, 2024
Hierarchical Multi-Agent Reinforcement Learning for Autonomous Cyber Defense in Coalition Networks.
Proceedings of the IEEE Military Communications Conference, 2024
BRETAGNE: Building a Reproducible and Efficient Training AI Gym for Network Environments.
Proceedings of the IEEE Military Communications Conference, 2024
Exploring the Potential of Large Language Models for Red Teaming in Military Coalition Networks.
Proceedings of the IEEE Military Communications Conference, 2024
2023
Proceedings of the IEEE Military Communications Conference, 2023
2022
Adversarial Attacks Against Reinforcement Learning Based Tactical Networks: A Case Study.
Proceedings of the IEEE Military Communications Conference, 2022