This file was created by the TYPO3 extension bib --- Timezone: UTC Creation date: 2024-11-21 Creation time: 12-05-59 --- Number of references 23 inproceedings 2024-wolsing-deployment 2024 9 With the escalating threats posed by cyberattacks on Industrial Control Systems (ICSs), the development of customized Industrial Intrusion Detection Systems (IIDSs) received significant attention in research. While existing literature proposes effective IIDS solutions evaluated in controlled environments, their deployment in real-world industrial settings poses several challenges. This paper highlights two critical yet often overlooked aspects that significantly impact their practical deployment, i.e., the need for sufficient amounts of data to train the IIDS models and the challenges associated with finding suitable hyperparameters, especially for IIDSs training only on genuine ICS data. Through empirical experiments conducted on multiple state-of-the-art IIDSs and diverse datasets, we establish the criticality of these issues in deploying IIDSs. Our findings show the necessity of extensive malicious training data for supervised IIDSs, which can be impractical considering the complexity of recording and labeling attacks in actual industrial environments. Furthermore, while other IIDSs circumvent the previous issue by requiring only benign training data, these can suffer from the difficulty of setting appropriate hyperparameters, which likewise can diminish their performance. By shedding light on these challenges, we aim to enhance the understanding of the limitations and considerations necessary for deploying effective cybersecurity solutions in ICSs, which might be one reason why IIDSs see few deployments. Industrial Intrusion Detection Systems, Cyber-Physical Systems, Industrial Control Systems, Deployment https://arxiv.org/pdf/2403.01809 Springer Proceedings of the 10th Workshop on the Security of Industrial Control Systems & of Cyber-Physical Systems (CyberICPS '24), co-located with the the 29th European Symposium on Research in Computer Security (ESORICS '24) Bydgoszcz, Poland 10th Workshop on the Security of Industrial Control Systems & of Cyber-Physical Systems (CyberICPS 2024) September 16-20, 2024 accepted English 1 KonradWolsing EricWagner FrederikBasels PatrickWagner KlausWehrle inproceedings 2024-wagner-madtls 2024 7 1 https://www.comsys.rwth-aachen.de/fileadmin/papers/2024/2024-wagner-madtls.pdf ACM 19th ACM ASIA Conference on Computer and Communications Security (ACM AsiaCCS '24), Singapur Singapur ACM ASIA Conference on Computer and Communications Security (AsiaCCS) July 1-5, 2024 10.1145/3634737.3637640 1 EricWagner DavidHeye MartinSerror IkeKunze KlausWehrle MartinHenze incollection 2024_pennekamp_blockchain-industry 2024 3 7 105 531-564 Competitive industrial environments impose significant requirements on data sharing as well as the accountability and verifiability of related processes. Here, blockchain technology emerges as a possible driver that satisfies demands even in settings with mutually distrustful stakeholders. We identify significant benefits achieved by blockchain technology for Industry 4.0 but also point out challenges and corresponding design options when applying blockchain technology in the industrial domain. Furthermore, we survey diverse industrial sectors to shed light on the current intersection between blockchain technology and industry, which provides the foundation for ongoing as well as upcoming research. As industrial blockchain applications are still in their infancy, we expect that new designs and concepts will develop gradually, creating both supporting tools and groundbreaking innovations. internet-of-production Springer Advances in Information Security 17 Blockchains – A Handbook on Fundamentals, Platforms and Applications 978-3-031-32145-0 10.1007/978-3-031-32146-7_17 1 JanPennekamp LennartBader EricWagner JensHiller RomanMatzutt KlausWehrle inproceedings 2024-wagner-acns-aggregate 2024 3 5 https://www.comsys.rwth-aachen.de/fileadmin/papers/2024/2024-wagner-mac-aggregation.pdf 22nd International Conference on Applied Cryptography and Network Security (ACNS '24), Abu Dhabi, UAE Abu Dhabi, UAE International Conference on Applied Cryptography and Network Security (ACNS) March 5-9, 2024 accepted 1 EricWagner MartinSerror KlausWehrle MartinHenze article 2024_pennekamp_supply-chain-survey ACM Computing Surveys 2024 2 1 56 2 Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010--2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points. information flows; data communication; supply chain management; data security; data sharing; systematic literature review internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2024/2024-pennekamp-supply-chain-survey.pdf ACM 0360-0300 10.1145/3606693 1 JanPennekamp RomanMatzutt ChristopherKlinkmüller LennartBader MartinSerror EricWagner SidraMalik MariaSpiß JessicaRahn TanGürpinar EduardVlad Sander J. J.Leemans Salil S.Kanhere VolkerStich KlausWehrle article 2023_lamberts_metrics-sok Journal of Systems Research 2023 10 31 3 1 Industrial systems are increasingly threatened by cyberattacks with potentially disastrous consequences. To counter such attacks, industrial intrusion detection systems strive to timely uncover even the most sophisticated breaches. Due to its criticality for society, this fast-growing field attracts researchers from diverse backgrounds, resulting in 130 new detection approaches in 2021 alone. This huge momentum facilitates the exploration of diverse promising paths but likewise risks fragmenting the research landscape and burying promising progress. Consequently, it needs sound and comprehensible evaluations to mitigate this risk and catalyze efforts into sustainable scientific progress with real-world applicability. In this paper, we therefore systematically analyze the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. Our analysis of 609 publications shows that the rapid growth of this research field has positive and negative consequences. While we observe an increased use of public datasets, publications still only evaluate 1.3 datasets on average, and frequently used benchmarking metrics are ambiguous. At the same time, the adoption of newly developed benchmarking metrics sees little advancement. Finally, our systematic analysis enables us to provide actionable recommendations for all actors involved and thus bring the entire research field forward. internet-of-production, rfc https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-lamberts-metrics-sok.pdf eScholarship Publishing 2770-5501 10.5070/SR33162445 1 OlavLamberts KonradWolsing EricWagner JanPennekamp JanBauer KlausWehrle MartinHenze 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 10.1007/978-3-031-54204-6_2 1 LennartBader EricWagner MartinHenze MartinSerror inproceedings 2023_wolsing_ensemble 2023 9 25 14345 102-122 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. Lecture Notes in Computer Science (LNCS), Volume 14345 Intrusion Detection; Ensemble Learning; ICS internet-of-production, rfc 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 28th European Symposium on Research in Computer Security (ESORICS '23) September 25-29, 2023 978-3-031-51475-3 0302-9743 10.1007/978-3-031-51476-0_6 1 KonradWolsing DominikKus EricWagner JanPennekamp KlausWehrle MartinHenze inproceedings 2022_kus_ensemble 2022 12 8 RWTH-2022-10809 Industrial intrusion detection promises to protect networked industrial control systems by monitoring them and raising an alarm in case of suspicious behavior. Many monolithic intrusion detection systems are proposed in literature. These detectors are often specialized and, thus, work particularly well on certain types of attacks or monitor different parts of the system, e.g., the network or the physical process. Combining multiple such systems promises to leverage their joint strengths, allowing the detection of a wider range of attacks due to their diverse specializations and reducing false positives. We study this concept's feasibility with initial results of various methods to combine detectors. rfc https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/2022-kus-ensemble-poster.pdf RWTH Aachen University 38th Annual Computer Security Applications Conference (ACSAC '22), December 5-9, 2022, Austin, TX, USA RWTH Aachen University Austin, TX, USA 38th Annual Computer Security Applications Conference (ACSAC '22) December 5-9, 2022 10.18154/RWTH-2022-10809 1 DominikKus KonradWolsing JanPennekamp EricWagner MartinHenze KlausWehrle inproceedings 2022-wolsing-ipal 2022 10 26 The increasing interconnection of industrial networks exposes them to an ever-growing risk of cyber attacks. To reveal such attacks early and prevent any damage, industrial intrusion detection searches for anomalies in otherwise predictable communication or process behavior. However, current efforts mostly focus on specific domains and protocols, leading to a research landscape broken up into isolated silos. Thus, existing approaches cannot be applied to other industries that would equally benefit from powerful detection. To better understand this issue, we survey 53 detection systems and find no fundamental reason for their narrow focus. Although they are often coupled to specific industrial protocols in practice, many approaches could generalize to new industrial scenarios in theory. To unlock this potential, we propose IPAL, our industrial protocol abstraction layer, to decouple intrusion detection from domain-specific industrial protocols. After proving IPAL’s correctness in a reproducibility study of related work, we showcase its unique benefits by studying the generalizability of existing approaches to new datasets and conclude that they are indeed not restricted to specific domains or protocols and can perform outside their restricted silos. /fileadmin/papers/2022/2022-wolsing-ipal.pdf Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2022) 10.1145/3545948.3545968 1 KonradWolsing EricWagner AntoineSaillard MartinHenze proceedings 2022-wolsing-radarsec 2022 9 rfc https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/2022-wolsing-radar.pdf IEEE Edmonton, Canada 47th IEEE Conference on Local Computer Networks (LCN) September 26-29, 2022 10.1109/LCN53696.2022.9843801 1 KonradWolsing AntoineSaillard JanBauer EricWagner Christianvan Sloun Ina BereniceFink MariSchmidt KlausWehrle MartinHenze inproceedings 2022-wolsing-simple 2022 9 978-3-031-17143-7 574--594 Cyberattacks against industrial control systems pose a serious risk to the safety of humans and the environment. Industrial intrusion detection systems oppose this threat by continuously monitoring industrial processes and alerting any deviations from learned normal behavior. To this end, various streams of research rely on advanced and complex approaches, i.e., artificial neural networks, thus achieving allegedly high detection rates. However, as we show in an analysis of 70 approaches from related work, their inherent complexity comes with undesired properties. For example, they exhibit incomprehensible alarms and models only specialized personnel can understand, thus limiting their broad applicability in a heterogeneous industrial domain. Consequentially, we ask whether industrial intrusion detection indeed has to be complex or can be SIMPLE instead, i.e., Sufficient to detect most attacks, Independent of hyperparameters to dial-in, Meaningful in model and alerts, Portable to other industrial domains, Local to a part of the physical process, and computationally Efficient. To answer this question, we propose our design of four SIMPLE industrial intrusion detection systems, such as simple tests for the minima and maxima of process values or the rate at which process values change. Our evaluation of these SIMPLE approaches on four state-of-the-art industrial security datasets reveals that SIMPLE approaches can perform on par with existing complex approaches from related work while simultaneously being comprehensible and easily portable to other scenarios. Thus, it is indeed justified to raise the question of whether industrial intrusion detection needs to be inherently complex. https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/2022-wolsing-simple.pdf Atluri, Vijayalakshmi and Di Pietro, Roberto and Jensen, Christian D. and Meng, Weizhi Springer Nature Switzerland Proceedings of the 27th European Symposium on Research in Computer Security (ESORICS '22), September 26-30, 2022, Copenhagen, Denmark Copenhagen, Denmark 27th European Symposium on Research in Computer Security (ESORICS) September 26-30, 2022 10.1007/978-3-031-17143-7_28 1 KonradWolsing LeaThiemt Christianvan Sloun EricWagner KlausWehrle MartinHenze proceedings 2022-serror-cset 2022 8 8 5 data sets, network traffic, smart grid security, IDS https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/2022-serror-cset-powerduck.pdf ACM

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online Virtual Cyber Security Experimentation and Test Workshop (CSET 2022) August 8, 2022 978-1-4503-9684-4/22/08 10.1145/3546096.3546102 1 SvenZemanek ImmanuelHacker KonradWolsing EricWagner MartinHenze MartinSerror 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 inproceedings WagnerSWH2022 2022 5 18 201-206 /fileadmin/papers/2022/2022-wagner-bpmac.pdf ACM Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '22) San Antonio, Texas, USA 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '22) 978-1-4503-9216-7/22/05 10.1145/3507657.3528554 1 EricWagner MartinSerror KlausWehrle MartinHenze inproceedings WagnerBH2022 2022 5 18 207-221 /fileadmin/papers/2022/2022-wagner-r2d2.pdf ACM Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '22) San Antonio, Texas, USA 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '22) 978-1-4503-9216-7/22/05 10.1145/3507657.3528539 1 EricWagner JanBauer MartinHenze inproceedings 2022_wagner_ccchain 2022 5 4 Blockchain technology promises to overcome trust and privacy concerns inherent to centralized information sharing. However, current decentralized supply chain management systems do either not meet privacy and scalability requirements or require a trustworthy consortium, which is challenging for increasingly dynamic supply chains with constantly changing participants. In this paper, we propose CCChain, a scalable and privacy-aware supply chain management system that stores all information locally to give companies complete sovereignty over who accesses their data. Still, tamper protection of all data through a permissionless blockchain enables on-demand tracking and tracing of products as well as reliable information sharing while affording the detection of data inconsistencies. Our evaluation confirms that CCChain offers superior scalability in comparison to alternatives while also enabling near real-time tracking and tracing for many, less complex products. supply chain management; blockchain; permissionless; deployment; tracing and tracking; privacy internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2022/2022-wagner-ccchain.pdf IEEE Proceedings of the 2022 IEEE International Conference on Blockchain and Cryptocurrency (ICBC '22), May 2-5, 2022, Shanghai, China Shanghai, China May 2-5, 2022 978-1-6654-9538-7/22 10.1109/ICBC54727.2022.9805503 1 EricWagner RomanMatzutt JanPennekamp LennartBader IrakliBajelidze KlausWehrle MartinHenze inproceedings 2021_pennekamp_laser 2021 12 21 Following the recent Internet of Things-induced trends on digitization in general, industrial applications will further evolve as well. With a focus on the domains of manufacturing and production, the Internet of Production pursues the vision of a digitized, globally interconnected, yet secure environment by establishing a distributed knowledge base. Background. As part of our collaborative research of advancing the scope of industrial applications through cybersecurity and privacy, we identified a set of common challenges and pitfalls that surface in such applied interdisciplinary collaborations. Aim. Our goal with this paper is to support researchers in the emerging field of cybersecurity in industrial settings by formalizing our experiences as reference for other research efforts, in industry and academia alike. Method. Based on our experience, we derived a process cycle of performing such interdisciplinary research, from the initial idea to the eventual dissemination and paper writing. This presented methodology strives to successfully bootstrap further research and to encourage further work in this emerging area. Results. Apart from our newly proposed process cycle, we report on our experiences and conduct a case study applying this methodology, raising awareness for challenges in cybersecurity research for industrial applications. We further detail the interplay between our process cycle and the data lifecycle in applied research data management. Finally, we augment our discussion with an industrial as well as an academic view on this research area and highlight that both areas still have to overcome significant challenges to sustainably and securely advance industrial applications. Conclusions. With our proposed process cycle for interdisciplinary research in the intersection of cybersecurity and industrial application, we provide a foundation for further research. We look forward to promising research initiatives, projects, and directions that emerge based on our methodological work. internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2021/2021-pennekamp-laser-collaboration.pdf ACSA Proceedings of the Workshop on Learning from Authoritative Security Experiment Results (LASER '20), co-located with the 36th Annual Computer Security Applications Conference (ACSAC '20), December 7-11, 2020, Austin, TX, USA Austin, TX, USA Learning from Authoritative Security Experiment Results (LASER '20) December 8, 2020 978-1-891562-81-5 10.14722/laser-acsac.2020.23088 1 JanPennekamp ErikBuchholz MarkusDahlmanns IkeKunze StefanBraun EricWagner MatthiasBrockmann KlausWehrle MartinHenze inproceedings 2020-wolsing-facilitating 2020 11 9 Cyber-physical systems are increasingly threatened by sophisticated attackers, also attacking the physical aspect of systems. Supplementing protective measures, industrial intrusion detection systems promise to detect such attacks. However, due to industrial protocol diversity and lack of standard interfaces, great efforts are required to adapt these technologies to a large number of different protocols. To address this issue, we identify existing universally applicable intrusion detection approaches and propose a transcription for industrial protocols to realize protocol-independent semantic intrusion detection on top of different industrial protocols. Intrusion Detection; IDS; Industrial Protocols; CPS; IEC-60870-5-104; Modbus; NMEA 0183 https://www.comsys.rwth-aachen.de/fileadmin/papers/2020/2020-wolsing-facilitating.pdf ACM

New York, NY, USA

Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (CCS ’20), November 9–13, 2020, Virtual Event, USA. Virtual Event, USA November 9-13, 2020 10.1145/3372297.3420019 1 KonradWolsing EricWagner MartinHenze inproceedings 2020-serror-networking-qwin 2020 6 21 consent https://comsys.rwth-aachen.de/fileadmin/papers/2020/2020-serror-qwin.pdf https://ieeexplore.ieee.org/abstract/document/9142792 IFIP online Proceedings of the 19th IFIP Networking 2020 Conference (NETWORKING '20), June 22-26, 2020, Paris, France Paris, France IFIP NETWORKING Conference June 22-26, 2020 978-3-903176-28-7 1 MartinSerror EricWagner RenéGlebke KlausWehrle inproceedings 2019_wagner_dispute_resolution 2019 5 Blockchain systems promise to mediate interactions of mutually distrusting parties without a trusted third party. However, protocols with full smart contract-based security are either limited in functionality or complex, with high costs for secured interactions. This observation leads to the development of protocol-specific schemes to avoid costly dispute resolution in case all participants remain honest. In this paper, we introduce SmartJudge, an extensible generalization of this trend for smart contract-based two-party protocols. SmartJudge relies on a protocol-independent mediator smart contract that moderates two-party interactions and only consults protocol-specific verifier smart contracts in case of a dispute. This way, SmartJudge avoids verification costs in absence of disputes and sustains interaction confidentiality among honest parties. We implement verifier smart contracts for cross-blockchain trades and exchanging digital goods and show that SmartJudge can reduce costs by 46-50% and 22% over current state of the art, respectively. Ethereum,Bitcoin,smart contracts,two-party protocols,dispute resolution,cross-blockchain trades mynedata, impact-digital, rfc https://www.comsys.rwth-aachen.de/fileadmin/papers/2019/2019-wagner-dispute.pdf IEEE IEEE International Conference on Blockchain and Cryptocurrency 2019 (ICBC 2019) Seoul, South Korea IEEE International Conference on Blockchain and Cryptocurrency 2019 English 10.1109/BLOC.2019.8751312 1 EricWagner AchimVölker FrederikFuhrmann RomanMatzutt KlausWehrle inproceedings 2018-hiller-lcn-lowlatencyiiot 2018 10 connect,iop,nerd-nrw https://www.comsys.rwth-aachen.de/fileadmin/papers/2018/2018-hiller-lcn-secure_low_latency_communication_iiot.pdf IEEE 43rd IEEE Conference on Local Computer Networks (LCN), Chicago, USA Chicago, USA 43nd IEEE Conference on Local Computer Networks (LCN) October 1-4, 2018 en 978-1-5386-4413-3 10.1109/LCN.2018.8638027 1 JensHiller MartinHenze MartinSerror EricWagner Jan NiklasRichter KlausWehrle