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@Proceedings { 2022-wolsing-radarsec,
   title = {Network Attacks Against Marine Radar Systems: A Taxonomy, Simulation Environment, and Dataset},
   year = {2022},
   month = {9},
   tags = {rfc},
   url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/2022-wolsing-radar.pdf},
   publisher = {IEEE},
   event_place = {Edmonton, Canada},
   event_name = {47th IEEE Conference on Local Computer Networks (LCN)},
   event_date = {September 26-29, 2022},
   DOI = {10.1109/LCN53696.2022.9843801},
   reviewed = {1},
   author = {Wolsing, Konrad and Saillard, Antoine and Bauer, Jan and Wagner, Eric and van Sloun, Christian and Fink, Ina Berenice and Schmidt, Mari and Wehrle, Klaus and Henze, Martin}
}

@Inproceedings { 2022_kus_iids_generalizability,
   title = {A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion Detection},
   year = {2022},
   month = {5},
   day = {30},
   pages = {73-84},
   abstract = {Anomaly-based intrusion detection promises to detect novel or unknown attacks on industrial control systems by modeling expected system behavior and raising corresponding alarms for any deviations. As manually creating these behavioral models is tedious and error-prone, research focuses on machine learning to train them automatically, achieving detection rates upwards of 99 \%. However, these approaches are typically trained not only on benign traffic but also on attacks and then evaluated against the same type of attack used for training. Hence, their actual, real-world performance on unknown (not trained on) attacks remains unclear. In turn, the reported near-perfect detection rates of machine learning-based intrusion detection might create a false sense of security. To assess this situation and clarify the real potential of machine learning-based industrial intrusion detection, we develop an evaluation methodology and examine multiple approaches from literature for their performance on unknown attacks (excluded from training). Our results highlight an ineffectiveness in detecting unknown attacks, with detection rates dropping to between 3.2 \% and 14.7 \% for some types of attacks. Moving forward, we derive recommendations for further research on machine learning-based approaches to ensure clarity on their ability to detect unknown attacks.},
   keywords = {anomaly detection; machine learning; industrial control system},
   tags = {internet-of-production, rfc},
   url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/2022-kus-iids-generalizability.pdf},
   publisher = {ACM},
   booktitle = {Proceedings of the 8th ACM Cyber-Physical System Security Workshop (CPSS '22), co-located with the 17th ACM ASIA Conference on Computer and Communications Security (ASIACCS '22), May 30-June 3, 2022, Nagasaki, Japan},
   ISBN = {978-1-4503-9176-4/22/05},
   DOI = {10.1145/3494107.3522773},
   reviewed = {1},
   author = {Kus, Dominik and Wagner, Eric and Pennekamp, Jan and Wolsing, Konrad and Fink, Ina Berenice and Dahlmanns, Markus and Wehrle, Klaus and Henze, Martin}
}

@Inproceedings { lorenz-ven2us-2022,
   title = {Interconnected network protection systems - the basis for the reliable and safe operation of distribution grids with a high penetration of renewable energies and electric vehicle},
   year = {2022},
   abstract = {Power grids are increasingly faced with the introduction of decentralized, highly volatile power supplies from renewable energies and high loads occurring from e-mobility. However, today’s static grid protection cannot manage all upcoming conditions while providing a high level of dependability and security. It forms a bottleneck of a future decarbonizing grid development. 

In our research project, we develop and verify an adaptive grid protection algorithm. It calculates situation dependent protection parameters for the event of power flow shifts and topology changes caused by volatile power supplies due to the increase of renewable generation and the rapid expansion of e-mobility. As a result the distribution grid can be operated with the optimally adapted protection parameters and functions for changing operating states. To safely adjust the values on protection hardware in the field, i.e., safe from hardware failures and cyberattacks, we research resilient and secure communication concepts for the adaptive and interconnected grid protection system. Finally, we validate our concept and system by demonstrations in the laboratory and field tests.},
   tags = {ven2us},
   booktitle = {Proceedings of the CIRED workshop on E-mobility and power distribution systems 2022, June 2-3, 2022, Porto, Portugal},
   event_place = {Porto},
   event_name = {CIRED workshop on E-mobility and power distribution systems 2022},
   event_date = {June 2-3, 2022},
   DOI = {10.1049/icp.2022.0768},
   reviewed = {1},
   author = {Lorenz, Matthias and Pletzer, Tobias Markus and Schuhmacher, Malte and Sowa, Torsten and Dahms, Michael and Stock, Simon and Babazadeh, Davood and Becker, Christian and Jaeger, Johann and Lorz, Tobias and Dahlmanns, Markus and Fink, Ina Berenice and Wehrle, Klaus and Ulbig, Andreas and Linnartz, Philipp and Selimaj, Antigona and Offergeld, Thomas}
}