This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2023-09-23 Creation time: 12-33-40 --- Number of references 5 inproceedings 2023-wagner-lcn-repel 2023 10 https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-wagner-repel.pdf IEEE 48th IEEE Conference on Local Computer Networks (LCN), Daytona Beach, Florida, US Daytona Beach, Florida, US IEEE Conference on Local Computer Networks (LCN) Oktober 1-5, 2023 accepted en 1 EricWagner NilsRothaug KonradWolsing LennartBader KlausWehrle MartinHenze inproceedings 2023-bader-metrics 2023 9 28 https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-bader-metrics.pdf Proceedings of the 9th Workshop on the Security of Industrial Control Systems & of Cyber-Physical Systems (CyberICPS '23), co-located with the the 28th European Symposium on Research in Computer Security (ESORICS '23) The Hague, The Netherlands 9th Workshop on the Security of Industrial Control Systems & of Cyber-Physical Systems (CyberICPS '23) September 28, 2023 accepted 1 LennartBader EricWagner MartinHenze MartinSerror inproceedings 2023_wolsing_ensemble 2023 9 25 Industrial Intrusion Detection Systems (IIDSs) play a critical role in safeguarding Industrial Control Systems (ICSs) against targeted cyberattacks. Unsupervised anomaly detectors, capable of learning the expected behavior of physical processes, have proven effective in detecting even novel cyberattacks. While offering decent attack detection, these systems, however, still suffer from too many False-Positive Alarms (FPAs) that operators need to investigate, eventually leading to alarm fatigue. To address this issue, in this paper, we challenge the notion of relying on a single IIDS and explore the benefits of combining multiple IIDSs. To this end, we examine the concept of ensemble learning, where a collection of classifiers (IIDSs in our case) are combined to optimize attack detection and reduce FPAs. While training ensembles for supervised classifiers is relatively straightforward, retaining the unsupervised nature of IIDSs proves challenging. In that regard, novel time-aware ensemble methods that incorporate temporal correlations between alerts and transfer-learning to best utilize the scarce training data constitute viable solutions. By combining diverse IIDSs, the detection performance can be improved beyond the individual approaches with close to no FPAs, resulting in a promising path for strengthening ICS cybersecurity. Intrusion Detection; Ensemble Learning; ICS internet-of-production https://jpennekamp.de/wp-content/papercite-data/pdf/wkw+23.pdf Springer Proceedings of the 28th European Symposium on Research in Computer Security (ESORICS '23), September 25-29, 2023, The Hague, The Netherlands The Hague, The Netherlands September 25-29, 2023 accepted 1 KonradWolsing DominikKus EricWagner JanPennekamp KlausWehrle MartinHenze inproceedings 2023_pennekamp_benchmarking_comparison 2023 6 15 489-505 Benchmarking is an essential tool for industrial organizations to identify potentials that allows them to improve their competitive position through operational and strategic means. However, the handling of sensitive information, in terms of (i) internal company data and (ii) the underlying algorithm to compute the benchmark, demands strict (technical) confidentiality guarantees—an aspect that existing approaches fail to address adequately. Still, advances in private computing provide us with building blocks to reliably secure even complex computations and their inputs, as present in industry benchmarks. In this paper, we thus compare two promising and fundamentally different concepts (hardware- and software-based) to realize privacy-preserving benchmarks. Thereby, we provide detailed insights into the concept-specific benefits. Our evaluation of two real-world use cases from different industries underlines that realizing and deploying secure information systems for industry benchmarking is possible with today's building blocks from private computing. Lecture Notes in Computer Science (LNCS), Volume 13901 real-world computing; trusted execution environments; homomorphic encryption; key performance indicators; benchmarking internet-of-production https://jpennekamp.de/wp-content/papercite-data/pdf/plv+23.pdf Springer Proceedings of the 35th International Conference on Advanced Information Systems Engineering (CAiSE '23), June 12-16, 2023, Zaragoza, Spain Zaragoza, Spain 35th International Conference on Advanced Information Systems Engineering (CAiSE '23) June 12-16, 2023 978-3-031-34559-3 0302-9743 10.1007/978-3-031-34560-9_29 1 JanPennekamp JohannesLohmöller EduardVlad JoschaLoos NiklasRodemann PatrickSapel Ina BereniceFink SethSchmitz ChristianHopmann MatthiasJarke GüntherSchuh KlausWehrle MartinHenze incollection 2023_pennekamp_crd-a.i 2023 2 8 The Internet of Production (IoP) leverages concepts such as digital shadows, data lakes, and a World Wide Lab (WWL) to advance today’s production. Consequently, it requires a technical infrastructure that can support the agile deployment of these concepts and corresponding high-level applications, which, e.g., demand the processing of massive data in motion and at rest. As such, key research aspects are the support for low-latency control loops, concepts on scalable data stream processing, deployable information security, and semantically rich and efficient long-term storage. In particular, such an infrastructure cannot continue to be limited to machines and sensors, but additionally needs to encompass networked environments: production cells, edge computing, and location-independent cloud infrastructures. Finally, in light of the envisioned WWL, i.e., the interconnection of production sites, the technical infrastructure must be advanced to support secure and privacy-preserving industrial collaboration. To evolve today’s production sites and lay the infrastructural foundation for the IoP, we identify five broad streams of research: (1) adapting data and stream processing to heterogeneous data from distributed sources, (2) ensuring data interoperability between systems and production sites, (3) exchanging and sharing data with different stakeholders, (4) network security approaches addressing the risks of increasing interconnectivity, and (5) security architectures to enable secure and privacy-preserving industrial collaboration. With our research, we evolve the underlying infrastructure from isolated, sparsely networked production sites toward an architecture that supports high-level applications and sophisticated digital shadows while facilitating the transition toward a WWL. Cyber-physical production systems; Data streams; Industrial data processing; Industrial network security; Industrial data security; Secure industrial collaboration internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-pennekamp-iop-a.i.pdf Springer Internet of Production: Fundamentals, Applications and Proceedings 978-3-030-98062-7 10.1007/978-3-030-98062-7_2-1 1 JanPennekamp AnastasiiaBelova ThomasBergs MatthiasBodenbenner AndreasBührig-Polaczek MarkusDahlmanns IkeKunze MoritzKröger SandraGeisler MartinHenze DanielLütticke BenjaminMontavon PhilippNiemietz LuciaOrtjohann MaximilianRudack Robert H.Schmitt UweVroomen KlausWehrle MichaelZeng