Publications

Henrik Madsen, Gudmund Grov, Federico Mancini, Magnus Baksaas, Åvald Åslaugson Sommervoll. Exploring Reinforcement Learning for Incident Response in Autonomous Military Vehicles. In: Proceedings of DIGILIENCE 2024. arXiv preprint arXiv:2410.21407. https://doi.org/10.48550/arXiv.2410.21407 

A. Jaber, M. Endregard, F. Mancini, G. Grov. Mission-Aware Cyber Incident Response Generation Using Reinforcement Learning. In: Proceedings of the NATO Science and Technology Organization Symposium (ICMCIS 2025), IST-209-RSY. Oeiras, Portugal, 13–14 May 2025. NATO STO, 2025. - Best Paper Award!    

G. Grov, J. Halvorsen, M. Eckhoff, B.J. Hansen, M. Eian, V. Mavroeidis, On the Use of Neurosymbolic AI for Defending Against Cyber Attacks. In: Besold, T.R., d’Avila Garcez, A., Jimenez-Ruiz, E., Confalonieri, R., Madhyastha, P., Wagner, B. (eds) Neural-Symbolic Learning and Reasoning. NeSy 2024. Lecture Notes in Computer Science, vol 14979. Springer, Cham. https://doi.org/10.1007/978-3-031-71167-1_7 

A. Lemesle, A. Varasse, Z. Chihani, D. Tachet, AIMOS: Metamorphic Testing of AI - An Industrial Application. In: Guiochet, J., Tonetta, S., Schoitsch, E., Roy, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops. SAFECOMP 2023. Lecture Notes in Computer Science, vol 14182. Springer, Cham. https://doi.org/10.1007/978-3-031-40953-0_27

Max Landauer, Florian Skopik, Markus Wurzenberger, Teodor Sommestad, Henrik Karlzén. Benign User Activities that Trigger False Positives in Intrusion Detection Systems: An Expert Survey. In: Proceedings of the 6th Workshop on Recent Advances in Cyber Situational Awareness and Data-Centric Approaches (CyberSA 2024), co-located with ARES 2024, Ghent, Belgium, August 2024. [DOI pending]  

Max Landauer, Florian Skopik, and Markus Wurzenberger. A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-Based Anomaly Detection Techniques. Proc. ACM Softw. Eng. 1, FSE, Article 61 (July 2024), 22 pages. https://doi.org/10.1145/3660768 

Patrick Himler, Max Landauer, Florian Skopik, and Markus Wurzenberger, Towards Detecting Anomalies in Log-Event Sequences with Deep Learning: Open Research Challenges. In Proceedings of the 2023 European Interdisciplinary Cybersecurity Conference (EICC '23). Association for Computing Machinery, New York, NY, USA, 71–77. https://doi.org/10.1145/3590777.3590789 

Max Landauer, Sebastian Onder, Florian Skopik, Markus Wurzenberger, Deep learning for anomaly detection in log data: A survey, Machine Learning with Applications, Volume 12, 2023, 100470, ISSN 2666-8270,https://doi.org/10.1016/j.mlwa.2023.100470 

Patrick Himler, Max Landauer, Florian Skopik, Markus Wurzenberger, Anomaly detection in log-event sequences: A federated deep learning approach and open challenges, Machine Learning with Applications, Volume 16, 2024, 100554, ISSN 2666-8270, https://doi.org/10.1016/j.mlwa.2024.100554 

Zakaria Chihani, Trustworthy AI: Industry-Guided Tooling of the Methods. In Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI (CAIN '24). Association for Computing Machinery, New York, NY, USA, 245–246. https://doi.org/10.1145/3644815.3644970 

Aws Jaber, Transforming Cybersecurity Dynamics: Enhanced Self-Play Reinforcement Learning in Intrusion Detection and Prevention System, 2024 IEEE International Systems Conference (SysCon), Montreal, QC, Canada, 2024, pp. 1-8, https://doi.org/10.1109/SysCon61195.2024.10553626 

Markus Wurzenberger, Georg Höld, Max Landauer, Florian Skopik, Analysis of statistical properties of variables in log data for advanced anomaly detection in cyber security, Computers & Security, Volume 137, 2024, 103631, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2023.103631 

Iker Antonio Olarra Maldonado, Puck de Haan, Erik Meeuwissen, Rob van der Mei, Telosian: Reducing False Positives in Real-Time Cyber Anomaly Detection by Fast Adaptation to Concept Drift. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 84-97. DOI: 10.5220/0013320500003899

Viktor Beck, Max Landauer, Markus Wurzenberger, Florian Skopik, Andreas Rauber, Semi-supervised Configuration and Optimization of Anomaly Detection Algorithms on Log Data, In Proc. 7th Annual Workshop on Cyber Threat Intelligence and Hunting (CyberHunt) in conjunction with the IEEE International Conference on Big Data 2024, December 15-18, 2024, Washington D.C., USA., https://doi.org/10.1109/BigData62323.2024.10825202 

Max Landauer, Klaus Mayer, Florian Skopik, Markus Wurzenberger, Manuel Kern, Red Team Redemption: A Structured Comparison of Open-Source Tools for Adversary Emulation, in Proc. 23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), December 17-21, 2024, Sanya, China. IEEE. Outstanding Paper Award, https://doi.ieeecomputersociety.org/10.1109/TrustCom63139.2024.00043

Max Landauer, Florian Skopik, Markus Wurzenberger, Introducing a New Alert Data Set for Multi-Step Attack Analysis, in Proc. Workshop on Cyber Security Experimentation and Test (CSET) colocated with the 33rd USENIX Security Symposium, August 14 - August 16, 2024, Philadelphia, PA, USA. Usenix., https://dl.acm.org/doi/10.1145/3675741.3675748

Aws Jaber, Giordano Colo, LB-MDP-CL: A Reinforcement Learning and Co-Evolutionary Approach for Optimizing Responses in Multi-Step Cyberattacks, in Proc. 23rd Mediterranean Communication and Computer Networking Conference (MedComNet), Cagliari, Italy, 2025,  https://ieeexplore.ieee.org/abstract/document/11103529

Aws Jaber, Maria Christopoulou, Giordano Colò, A Novel Approach to Automated Cybersecurity Response for Critical Infrastructures Using Graph Neural Networks and Reinforcement Learning, in Proc. 39th International Conference on Advanced Information Networking and Applications (AINA-2025), Volume 6, https://link.springer.com/chapter/10.1007/978-3-031-87778-0_4

Gustaf Johansson, Aws Jaber, Florian Skopik, Michael Landauer, Wolfgang Hotwagner, Martin Wurzenberger, Dynamic Shields: A Game-Theoretic Reinforcement Learning Framework for APT Mitigation, in Proc. 16th International Conference, GameSec 2025, Athens, Greece, October 13–15, 2025, Proceedings, Part I, https://link.springer.com/chapter/10.1007/978-3-032-08064-6_7

Aws Jaber , Gudmund Grov,  Angel D. Genchev, Giordano Colo, Stackelberg Security Game with Reinforcement Learning for Defending Video Servers Against APT Attacks, in Proc. 2025 4th International Conference on Computing, Management and Telecommunications (ComManTel) (accepted, pending publication) 

Aws Jaber; Angel D. Genchev; Gudmund Grov; Tsvetelin Tsonev, CyberStack: Deep Reinforcement Learning in Stackelberg Games for Military Network Defense, in Proc. IEEE Military Communications Conference 6–10 October 2025, Los Angeles, CA, USA, (accepted, pending publication) 

Magnus Wiik Eckhoff, Jonas Halvorsen, Bjørn Jervell Hansen, Martin Eian, Vasileios Mavroeidis, Robert Andrew Chetwyn, Geir Skjøtskift and Gudmund Grov, Experimenting with neurosymbolic AI for defending against cyber attacks, Neurosymbolic Artificial Intelligence Journal, IOS Press, https://doi.org/10.1177/29498732251377352

Magnus Wiik Eckhoff, Peter Marius Flydal, Siem Peters, Martin Eian, Jonas Halvorsen, Vasileios Mavroeidis and Gudmund Grov, A Graph-Based Approach to Alert Contextualisation in Security Operations Centres, in Proc. 28th Information Security Conference (ISC 2025), https://doi.org/10.1007/978-3-032-08124-7_24

Iker Antonio Olarra Maldonado, Puck de Haan, Fredrik Morten Sætran, Erik Meeuwissen, On Telosian for Real-Time Cyber Anomaly Detection: Evaluating Robustness to Concept Drift and Vulnerability to Adversarial Attacks, Computer Science (accepted, pending publication)