Supply chains form the backbone of modern economies and therefore require reliable information lows. 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 lows. We subsequently present a concise information low-focused taxonomy for supply chains before discussing future research directions to provide possible entry points.

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.

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.

Resource-constrained devices increasingly rely on wireless communication for the reliable and low-latency transmission of short messages. However, especially the implementation of adequate integrity protection of time-critical messages places a significant burden on these devices. We address this issue by proposing BP-MAC, a fast and memory-efficient approach for computing message authentication codes based on the well-established Carter-Wegman construction. Our key idea is to offload resource-intensive computations to idle phases and thus save valuable time in latency-critical phases, i.e., when new data awaits processing. Therefore, BP-MAC leverages a universal hash function designed for the bitwise preprocessing of integrity protection to later only require a few XOR operations during the latency-critical phase. Our evaluation on embedded hardware shows that BP-MAC outperforms the state-of-the-art in terms of latency and memory overhead, notably for small messages, as required to adequately protect resource-constrained devices with stringent security and latency requirements.

Message authentication guarantees the integrity of messages exchanged over untrusted channels. However, to achieve this goal, message authentication considerably expands packet sizes, which is especially problematic in constrained wireless environments. To address this issue, progressive message authentication provides initially reduced integrity protection that is often sufficient to process messages upon reception. This reduced security is then successively improved with subsequent messages to uphold the strong guarantees of traditional integrity protection. However, contrary to previous claims, we show in this paper that existing progressive message authentication schemes are highly susceptible to packet loss induced by poor channel conditions or jamming attacks. Thus, we consider it imperative to rethink how authentication tags depend on the successful reception of surrounding packets. To this end, we propose R2-D2, which uses randomized dependencies with parameterized security guarantees to increase the resilience of progressive authentication against packet loss. To deploy our approach to resource-constrained devices, we introduce SP-MAC, which implements R2-D2 using efficient XOR operations. Our evaluation shows that SP-MAC is resilient to sophisticated network-level attacks and operates as resources-conscious and fast as existing, yet insecure, progressive message authentication schemes.

Wireless communication in industrial networks is challenging due to low latency and high reliability requirements. In this paper, we propose a MAC layer based on distributed priority queues to take advantage of industrial networks’ heterogeneity and the fact that all industrial IoT devices within them pursue a common goal. Through several extensions, we increase the reliability of high-priority messages by several orders of magnitude without reducing less important messages’ reliability.

In this paper, we present SmartJudge, an extensible framework for secure and private trades of digital assets without a trusted third party. We designed efficient verifiers for ETH to BTC atomic swaps (by introducing a novel Bitcoin transaction verification algorithm) and fair data exchanges. In both cases, we achieve cheaper trades compared to the state-of-the-art and, additionally, provide contract confidentiality. SmartJudge optimistically assumes honest behavior by both parties and offers recovery mechanisms (paid for by the malicious actor) in case of misbehavior.

In this paper, we first survey latency requirements towards industrial Internet of Things (IIoT) devices. We then propose two optimization mechanisms for established AES-based encryption and authentication to help IIoT devices achieve their stringent latency requirements. These mechanisms work by enabling the preprocessing of cryptographic operations, yielding latency reductions of up to 75.9%.