Dr. Eric Wagner

While anonymity networks such as Tor provide invaluable privacy guarantees to society, they also enable all kinds of criminal activities. Consequently, many blameless citizens shy away from protecting their privacy using such technology for fear of being associated with criminals. To grasp the potential for alternative privacy protection for those users, we design Seldom , an anonymity network with integrated selective deanonymization that disincentivizes criminal activity. Seldom enables law enforcement agencies to selectively access otherwise anonymized identities of misbehaving users while providing technical guarantees preventing these access rights from being misused. Seldom further ensures translucency, as each access request is approved by a trustworthy consortium of impartial entities and eventually disclosed to the public (without interfering with ongoing investigations). To demonstrate Seldom ’s feasibility and applicability, we base our implementation on Tor, the most widely used anonymity network. Our evaluation indicates minimal latency, processing, and bandwidth overheads compared to Tor; Seldom’s main costs stem from storing flow records and encrypted identities. With at most (636 ,rm TB) of storage required in total to retain the encrypted identifiers of a Tor-sized network for two years, Seldom provides a practical and deployable technical solution to the inherent problem of criminal activities in anonymity networks. As such, Seldom sheds new light on the potentials and limitations when integrating selective deanonymization into anonymity networks.

Protecting industrial control systems against cyberattacks is crucial to counter escalating threats to critical infrastructure. To this end, Industrial Intrusion Detection Systems (IIDSs) provide an easily retrofittable approach to uncover attacks quickly and before they can cause significant damage. Current research focuses either on maximizing automation, usually through heavy use of machine learning, or on expert systems that rely on detailed knowledge of the monitored systems. While the former hinders the interpretability of alarms, the latter is impractical in real deployments due to excessive manual work for each individual deployment. To bridge the gap between maximizing automation and leveraging expert knowledge, we introduce GeCo, a novel IIDS based on automatically derived comprehensible models of benign system behavior. GeCo leverages state-space models mined from historical process data to minimize manual effort for operators while maintaining high detection performance and generalizability across diverse industrial domains. Our evaluation against state-of-the-art IIDSs and datasets demonstrates GeCo’s superior performance while remaining comprehensible and performing on par with expert-derived rules. GeCo represents a critical step towards empowering operators with control over their cybersecurity toolset, thereby enhancing the protection of valuable physical processes in industrial control systems and critical infrastructures.

Controller Area Networks (CANs) are the backbone for reliable intra-vehicular communication. Recent cyberattacks have, however, exposed the weaknesses of CAN, which was designed without any security considerations in the 1980s. Current efforts to retrofit security via intrusion detection or message authentication codes are insufficient to fully secure CAN as they cannot adequately protect against masquerading attacks, where a compromised communication device, a so-called electronic control units, imitates another device. To remedy this situation, multicast source authentication is required to reliably identify the senders of messages. In this paper, we present CAIBA, a novel multicast source authentication scheme specifically designed for communication buses like CAN. CAIBA relies on an authenticator overwriting authentication tags on-the-fly, such that a receiver only reads a valid tag if not only the integrity of a message but also its source can be verified. To integrate CAIBA into CAN, we devise a special message authentication scheme and a reactive bit overwriting mechanism. We achieve interoperability with legacy CAN devices, while protecting receivers implementing the AUTOSAR SecOC standard against masquerading attacks without communication overhead or verification delays.

Physically distributed components and legacy protocols make the protection of power grids against increasing cyberattack threats challenging. Infamously, the 2015 and 2016 blackouts in Ukraine were caused by cyberattacks, and the German Federal Office for Information Security (BSI) recorded over 200 cyber incidents against the German energy sector between 2023 and 2024. Intrusion detection promises to quickly detect such attacks and mitigate the worst consequences. However, public datasets of realistic scenarios are vital to evaluate these systems. This paper introduces Sherlock, a dataset generated with the co-simulator Wattson. In total, Sherlock covers three scenarios with various attacks manipulating the process state by injecting malicious commands or manipulating measurement values. We additionally test five recently-published intrusion detection systems on Sherlock, highlighting specific challenges for intrusion detection in power grids. Dataset and documentation are available at https://sherlock.wattson.it/.

Aggregating Message Authentication Codes (MACs) promises to save valuable bandwidth in resource-constrained environments. The idea is simple: Instead of appending an authentication tag to each message in a communication stream, the integrity protection of multiple messages is aggregated into a single tag. Recent studies postulate, e.g., based on simulations, that these benefits also spread to wireless, and thus lossy, scenarios despite each lost packet typically resulting in the loss of integrity protection information for multiple messages. In this paper, we investigate these claims in a real deployment. Therefore, we first design a MAC aggregation extension for the Datagram Transport Layer Security (DTLS) 1.3 protocol. Afterward, we extensively evaluate the performance of MAC aggregation on a complete communication protocol stack on embedded hardware. We find that MAC aggregation can indeed increase goodput by up to 50 % and save up to 17 % of energy expenditure for the transmission of short messages, even in lossy channels.

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.