This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-04-25 Creation time: 22-45-24 --- Number of references 5 inproceedings 2022_dahlmanns_tlsiiot 2022 5 31 252-266 The ongoing trend to move industrial appliances from previously isolated networks to the Internet requires fundamental changes in security to uphold secure and safe operation. Consequently, to ensure end-to-end secure communication and authentication, (i) traditional industrial protocols, e.g., Modbus, are retrofitted with TLS support, and (ii) modern protocols, e.g., MQTT, are directly designed to use TLS. To understand whether these changes indeed lead to secure Industrial Internet of Things deployments, i.e., using TLS-based protocols, which are configured according to security best practices, we perform an Internet-wide security assessment of ten industrial protocols covering the complete IPv4 address space. Our results show that both, retrofitted existing protocols and newly developed secure alternatives, are barely noticeable in the wild. While we find that new protocols have a higher TLS adoption rate than traditional protocols (7.2 % vs. 0.4 %), the overall adoption of TLS is comparably low (6.5 % of hosts). Thus, most industrial deployments (934,736 hosts) are insecurely connected to the Internet. Furthermore, we identify that 42 % of hosts with TLS support (26,665 hosts) show security deficits, e.g., missing access control. Finally, we show that support in configuring systems securely, e.g., via configuration templates, is promising to strengthen security. industrial communication; network security; security configuration internet-of-production, rfc https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/2022-dahlmanns-asiaccs.pdf ACM Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security (ASIACCS '22), May 30-June 3, 2022, Nagasaki, Japan Nagasaki, Japan ASIACCS '22 May 30-June 3, 2022 978-1-4503-9140-5/22/05 10.1145/3488932.3497762 1 MarkusDahlmanns JohannesLohmöller JanPennekamp JörnBodenhausen KlausWehrle MartinHenze inproceedings 2022_kus_iids_generalizability 2022 5 30 73-84 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. anomaly detection; machine learning; industrial control system internet-of-production, rfc https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/2022-kus-iids-generalizability.pdf ACM 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 978-1-4503-9176-4/22/05 10.1145/3494107.3522773 1 DominikKus EricWagner JanPennekamp KonradWolsing Ina BereniceFink MarkusDahlmanns KlausWehrle MartinHenze article 2022_brauner_iop ACM Transactions on Internet of Things 2022 5 1 3 2 The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production, two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI. Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure. Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality. Internet of Production; World Wide Lab; Digital Shadows; Industrial Internet of Things internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/2022-brauner-digital-transformation.pdf ACM 2691-1914 10.1145/3502265 1 PhilippBrauner ManuelaDalibor MatthiasJarke IkeKunze IstvánKoren GerhardLakemeyer MartinLiebenberg JudithMichael JanPennekamp ChristophQuix BernhardRumpe Wilvan der Aalst KlausWehrle AndreasWortmann MartinaZiefle techreport draft-irtf-coinrg-use-cases-02 2022 3 draft-irtf-coinrg-use-cases-02 expires: 8 September 2022 (work in progress) https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/draft-irtf-coinrg-use-cases-02.pdf https://datatracker.ietf.org/doc/draft-irtf-coinrg-use-cases/ Online IETF Trust Internet Drafts Internet Engineering Task Force Internet Engineering Task Force IkeKunze KlausWehrle DirkTrossen Marie-JoséMontpetit Xavierde Foy DavidGriffin MiguelRio inproceedings lorenz-ven2us-2022 2022 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. ven2us Proceedings of the CIRED workshop on E-mobility and power distribution systems 2022, June 2-3, 2022, Porto, Portugal Porto CIRED workshop on E-mobility and power distribution systems 2022 June 2-3, 2022 10.1049/icp.2022.0768 1 MatthiasLorenz Tobias MarkusPletzer MalteSchuhmacher TorstenSowa MichaelDahms SimonStock DavoodBabazadeh ChristianBecker JohannJaeger TobiasLorz MarkusDahlmanns Ina BereniceFink KlausWehrle AndreasUlbig PhilippLinnartz AntigonaSelimaj ThomasOffergeld