Klaus Wehrle

David Heye, Karl Kindermann, Robin Decker et al.

Artifact Evaluation (AE) is essential for ensuring the transparency and reliability of research, closing the gap between exploratory work and real-world deployment is particularly important in cybersecurity, particularly in IoT and CPSs, where large-scale, heterogeneous, and privacy-sensitive data meet safety-critical actuation. Yet, manual reproducibility checks are time-consuming and do not scale with growing submission volumes. In this work, we demonstrate that Large Language Models (LLMs) can provide powerful support for AE tasks: (i) text-based reproducibility rating, (ii) autonomous sandboxed execution environment preparation, and (iii) assessment of methodological pitfalls. Our reproducibility-assessment toolkit yields an accuracy of over 72% and autonomously sets up execution environments for 28% of runnable cybersecurity artifacts. Our automated pitfall assessment detects seven prevalent pitfalls with high accuracy ($F_1$ > 92%). Hence, the toolkit significantly reduces reviewer effort and, when integrated into established AE processes, could incentivize authors to submit higher-quality and more reproducible artifacts. IoT, CPS, and cybersecurity conferences and workshops may integrate the toolkit into their peer-review processes to support reviewers' decisions on awarding artifact badges, improving the overall sustainability of the process.

Dominik Kus, Eric Wagner, Jan Pennekamp et al.

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.

Jan Pennekamp, Erik Buchholz, Markus Dahlmanns et al.

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.

Roman Matzutt, Benedikt Kalde, Jan Pennekamp et al.

Popular cryptocurrencies continue to face serious scalability issues due to their ever-growing blockchains. Thus, modern blockchain designs began to prune old blocks and rely on recent snapshots for their bootstrapping processes instead. Unfortunately, established systems are often considered incapable of adopting these improvements. In this work, we present CoinPrune, our block-pruning scheme with full Bitcoin compatibility, to revise this popular belief. CoinPrune bootstraps joining nodes via snapshots that are periodically created from Bitcoin's set of unspent transaction outputs (UTXO set). Our scheme establishes trust in these snapshots by relying on CoinPrune-supporting miners to mutually reaffirm a snapshot's correctness on the blockchain. This way, snapshots remain trustworthy even if adversaries attempt to tamper with them. Our scheme maintains its retrospective deployability by relying on positive feedback only, i.e., blocks containing invalid reaffirmations are not rejected, but invalid reaffirmations are outpaced by the benign ones created by an honest majority among CoinPrune-supporting miners. Already today, CoinPrune reduces the storage requirements for Bitcoin nodes by two orders of magnitude, as joining nodes need to fetch and process only 6 GiB instead of 271 GiB of data in our evaluation, reducing the synchronization time of powerful devices from currently 7 h to 51 min, with even larger potential drops for less powerful devices. CoinPrune is further aware of higher-level application data, i.e., it conserves otherwise pruned application data and allows nodes to obfuscate objectionable and potentially illegal blockchain content from their UTXO set and the snapshots they distribute.

Martin Serror, Sacha Hack, Martin Henze et al.

Given the tremendous success of the Internet of Things in interconnecting consumer devices, we observe a natural trend to likewise interconnect devices in industrial settings, referred to as Industrial Internet of Things or Industry 4.0. While this coupling of industrial components provides many benefits, it also introduces serious security challenges. Although sharing many similarities with the consumer Internet of Things, securing the Industrial Internet of Things introduces its own challenges but also opportunities, mainly resulting from a longer lifetime of components and a larger scale of networks. In this paper, we identify the unique security goals and challenges of the Industrial Internet of Things, which, unlike consumer deployments, mainly follow from safety and productivity requirements. To address these security goals and challenges, we provide a comprehensive survey of research efforts to secure the Industrial Internet of Things, discuss their applicability, and analyze their security benefits.

Markus Dahlmanns, Johannes Lohmöller, Ina Berenice Fink et al.

Due to increasing digitalization, formerly isolated industrial networks, e.g., for factory and process automation, move closer and closer to the Internet, mandating secure communication. However, securely setting up OPC UA, the prime candidate for secure industrial communication, is challenging due to a large variety of insecure options. To study whether Internet-facing OPC UA appliances are configured securely, we actively scan the IPv4 address space for publicly reachable OPC UA systems and assess the security of their configurations. We observe problematic security configurations such as missing access control (on 24% of hosts), disabled security functionality (24%), or use of deprecated cryptographic primitives (25%) on in total 92% of the reachable deployments. Furthermore, we discover several hundred devices in multiple autonomous systems sharing the same security certificate, opening the door for impersonation attacks. Overall, in this paper, we highlight commonly found security misconfigurations and underline the importance of appropriate configuration for security-featuring protocols.

Daniel Schemmel, Julian Büning, César Rodríguez et al.

We describe a technique for systematic testing of multi-threaded programs. We combine Quasi-Optimal Partial-Order Reduction, a state-of-the-art technique that tackles path explosion due to interleaving non-determinism, with symbolic execution to handle data non-determinism. Our technique iteratively and exhaustively finds all executions of the program. It represents program executions using partial orders and finds the next execution using an underlying unfolding semantics. We avoid the exploration of redundant program traces using cutoff events. We implemented our technique as an extension of KLEE and evaluated it on a set of large multi-threaded C programs. Our experiments found several previously undiscovered bugs and undefined behaviors in memcached and GNU sort, showing that the new method is capable of finding bugs in industrial-size benchmarks.

Roman Matzutt, Benedikt Kalde, Jan Pennekamp et al.

Bitcoin was the first successful decentralized cryptocurrency and remains the most popular of its kind to this day. Despite the benefits of its blockchain, Bitcoin still faces serious scalability issues, most importantly its ever-increasing blockchain size. While alternative designs introduced schemes to periodically create snapshots and thereafter prune older blocks, already-deployed systems such as Bitcoin are often considered incapable of adopting corresponding approaches. In this work, we revise this popular belief and present CoinPrune, a snapshot-based pruning scheme that is fully compatible with Bitcoin. CoinPrune can be deployed through an opt-in velvet fork, i.e., without impeding the established Bitcoin network. By requiring miners to publicly announce and jointly reaffirm recent snapshots on the blockchain, CoinPrune establishes trust into the snapshots' correctness even in the presence of powerful adversaries. Our evaluation shows that CoinPrune reduces the storage requirements of Bitcoin already by two orders of magnitude today, with further relative savings as the blockchain grows. In our experiments, nodes only have to fetch and process 5 GiB instead of 230 GiB of data when joining the network, reducing the synchronization time on powerful devices from currently 5 h to 46 min, with even more savings for less powerful devices.

Roman Matzutt, Jan Pennekamp, Erik Buchholz et al.

Distributed anonymity services, such as onion routing networks or cryptocurrency tumblers, promise privacy protection without trusted third parties. While the security of these services is often well-researched, security implications of their required bootstrapping processes are usually neglected: Users either jointly conduct the anonymization themselves, or they need to rely on a set of non-colluding privacy peers. However, the typically small number of privacy peers enable single adversaries to mimic distributed services. We thus present AnonBoot, a Sybil-resistant medium to securely bootstrap distributed anonymity services via public blockchains. AnonBoot enforces that peers periodically create a small proof of work to refresh their eligibility for providing secure anonymity services. A pseudo-random, locally replicable bootstrapping process using on-chain entropy then prevents biasing the election of eligible peers. Our evaluation using Bitcoin as AnonBoot's underlying blockchain shows its feasibility to maintain a trustworthy repository of 1000 peers with only a small storage footprint while supporting arbitrarily large user bases on top of most blockchains.

Constantin Sander, Jan Rüth, Oliver Hohlfeld et al.

Transport protocols use congestion control to avoid overloading a network. Nowadays, different congestion control variants exist that influence performance. Studying their use is thus relevant, but it is hard to identify which variant is used. While passive identification approaches exist, these require detailed domain knowledge and often also rely on outdated assumptions about how congestion control operates and what data is accessible. We present DeePCCI, a passive, deep learning-based congestion control identification approach which does not need any domain knowledge other than training traffic of a congestion control variant. By only using packet arrival data, it is also directly applicable to encrypted (transport header) traffic. DeePCCI is therefore more easily extendable and can also be used with QUIC.

Felix Rath, Daniel Schemmel, Klaus Wehrle

The main reason for the standardization of network protocols, like QUIC, is to ensure interoperability between implementations, which poses a challenging task. Manual tests are currently used to test the different existing implementations for interoperability, but given the complex nature of network protocols, it is hard to cover all possible edge cases. State-of-the-art automated software testing techniques, such as Symbolic Execution (SymEx), have proven themselves capable of analyzing complex real-world software and finding hard to detect bugs. We present a SymEx-based method for finding interoperability issues in QUIC implementations, and explore its merit in a case study that analyzes the interoperability of picoquic and QUANT. We find that, while SymEx is able to analyze deep interactions between different implementations and uncovers several bugs, in order to enable efficient interoperability testing, implementations need to provide additional information about their current protocol state.

Martin Henze, René Hummen, Roman Matzutt et al.

The increasing deployment of sensor networks, ranging from home networks to industrial automation, leads to a similarly growing demand for storing and processing the collected sensor data. To satisfy this demand, the most promising approach to date is the utilization of the dynamically scalable, on-demand resources made available via the cloud computing paradigm. However, prevalent security and privacy concerns are a huge obstacle for the outsourcing of sensor data to the cloud. Hence, sensor data needs to be secured properly before it can be outsourced to the cloud. When securing the outsourcing of sensor data to the cloud, one important challenge lies in the representation of sensor data and the choice of security measures applied to it. In this paper, we present the SensorCloud protocol, which enables the representation of sensor data and actuator commands using JSON as well as the encoding of the object security mechanisms applied to a given sensor data item. Notably, we solely utilize mechanisms that have been or currently are in the process of being standardized at the IETF to aid the wide applicability of our approach.

Jan Henrik Ziegeldorf, Oscar Garcia Morchon, Klaus Wehrle

The Internet of Things paradigm envisions the pervasive interconnection and cooperation of smart things over the current and future Internet infrastructure. The Internet of Things is, thus, the evolution of the Internet to cover the real-world, enabling many new services that will improve people's everyday lives, spawn new businesses and make buildings, cities and transport smarter. Smart things allow indeed for ubiquitous data collection or tracking, but these useful features are also examples of privacy threats that are already now limiting the success of the Internet of Things vision when not implemented correctly. These threats involve new challenges such as the pervasive privacy-aware management of personal data or methods to control or avoid ubiquitous tracking and profiling. This paper analyzes the privacy issues in the Internet of Things in detail. To this end, we first discuss the evolving features and trends in the Internet of Things with the goal of scrutinizing their privacy implications. Second, we classify and examine privacy threats in this new setting, pointing out the challenges that need to be overcome to ensure that the Internet of Things becomes a reality.

Martin Henze, Lars Hermerschmidt, Daniel Kerpen et al.

Internet of Things devices are envisioned to penetrate essentially all aspects of life, including homes and urbanspaces, in use cases such as health care, assisted living, and smart cities. One often proposed solution for dealing with the massive amount of data collected by these devices and offering services on top of them is the federation of the Internet of Things and cloud computing. However, user acceptance of such systems is a critical factor that hinders the adoption of this promising approach due to severe privacy concerns. We present UPECSI, an approach for user-driven privacy enforcement for cloud-based services in the Internet of Things to address this critical factor. UPECSI enables enforcement of all privacy requirements of the user once her sensitive data leaves the border of her network, provides a novel approach for the integration of privacy functionality into the development process of cloud-based services, and offers the user an adaptable and transparent configuration of her privacy requirements. Hence, UPECSI demonstrates an approach for realizing user-accepted cloud services in the Internet of Things.

Jan Henrik Ziegeldorf, Nicolai Viol, Martin Henze et al.

Upcoming WiFi-based localization systems for indoor environments face a conflict of privacy interests: Server-side localization violates location privacy of the users, while localization on the user's device forces the localization provider to disclose the details of the system, e.g., sophisticated classification models. We show how Secure Two-Party Computation can be used to reconcile privacy interests in a state-of-the-art localization system. Our approach provides strong privacy guarantees for all involved parties, while achieving room-level localization accuracy at reasonable overheads.

Michael Eggert, Roger Häußling, Martin Henze et al.

Although Cloud Computing promises to lower IT costs and increase users' productivity in everyday life, the unattractive aspect of this new technology is that the user no longer owns all the devices which process personal data. To lower scepticism, the project SensorCloud investigates techniques to understand and compensate these adoption barriers in a scenario consisting of cloud applications that utilize sensors and actuators placed in private places. This work provides an interdisciplinary overview of the social and technical core research challenges for the trustworthy integration of sensor and actuator devices with the Cloud Computing paradigm. Most importantly, these challenges include i) ease of development, ii) security and privacy, and iii) social dimensions of a cloud-based system which integrates into private life. When these challenges are tackled in the development of future cloud systems, the attractiveness of new use cases in a sensor-enabled world will considerably be increased for users who currently do not trust the Cloud.