This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-04-25 Creation time: 18-36-35 --- Number of references 4 inproceedings 2023_sloun_accessibility 2023 11 27 3543-3545 The generation of datasets is one of the most promising approaches to collecting the necessary behavior data to train machine learning models for host-based intrusion detection. While various dataset generation methods have been proposed, they are often limited and either only generate network traffic or are restricted to a narrow subset of applications. We present Vulcan, a preliminary framework that uses accessibility features to generate datasets by simulating user interactions for an extendable set of applications. It uses behavior profiles that define realistic user behavior and facilitate dataset updates upon changes in software versions, thus reducing the effort required to keep a dataset relevant. Preliminary results show that using accessibility features presents a promising approach to improving the quality of datasets in the HIDS domain. Intrusion Detection, Dataset Generation, Accessibility Features https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-sloun-vulcan-accessibility.pdf ACM Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS '23), November 26-30, 2023, Copenhagen, Denmark Copenhagen, Denmark November 26-30, 2023 979-8-4007-0050-7/23/11 10.1145/3576915.3624404 1 Christianvan Sloun KlausWehrle inproceedings 2023_lohmoeller_transparency 2023 11 27 data usage control; data ecosystems; transparency logs https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-lohmoeller-transparency.pdf ACM Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS ’23), November 26-30, 2023, Copenhagen, Denmark Copenhagen, Denmark November 26-30, 2023 979-8-4007-0050-7/23/11 10.1145/3576915.3624371 1 JohannesLohmöller EduardVlad MarkusDahlmanns KlausWehrle inproceedings 2023_pennekamp_benchmarking_comparison 2023 6 15 13901 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 35-60 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 Interdisciplinary Excellence Accelerator Series Internet of Production: Fundamentals, Applications and Proceedings 978-3-031-44496-8 10.1007/978-3-031-44497-5_2 1 JanPennekamp AnastasiiaBelova ThomasBergs MatthiasBodenbenner AndreasBührig-Polaczek MarkusDahlmanns IkeKunze MoritzKröger SandraGeisler MartinHenze DanielLütticke BenjaminMontavon PhilippNiemietz LuciaOrtjohann MaximilianRudack Robert H.Schmitt UweVroomen KlausWehrle MichaelZeng