In this paper, we present a new methodology that enables the MITRE ATT&CK framework to objectively assess specific threats in 6G Radio Access Networks (RANs). This helps address new security challenges that arise in the transition to open RANs. We analyze the O-Cloud component within the O-RAN ecosystem as a representative example, wherein no individual threat class demonstrates complete security. The inherent modularity of our approach ensures great adaptability and allows it to be applied to various other components within this system. This allows us to effectively detect and combat threats, thereby ensuring the resilience and security of future communication networks.

Unlinkability describes the inability of an observer to decide whether certain items of interest are related or not. Privacy aware protocol designers need a consistent and meaningful unlinkability measure to asses protocols in face of different attacks. In this paper we show that entropy measures are not sufficient for measuring unlinkability. We propose an alternative measure that estimates the error made by an attacker. We show by example that our expected distance provides a consistent measure that offers a better estimation of message-unlinkability.

Timing-based side and covert channels in processor caches continue to be a threat to modern computers. This work shows for the first time, a systematic, large-scale analysis of Arm devices and the detailed results of attacks the processors are vulnerable to. Compared to x86, Arm uses different architectures, microarchitectural implementations, cache replacement policies, etc., which affects how attacks can be launched, and how security testing for the vulnerabilities should be done. To evaluate security, this paper presents security benchmarks specifically developed for testing Arm processors and their caches. The benchmarks are evaluated with sensitivity tests, which examine how sensitive the benchmarks are to having a correct configuration in the testing phase. Further, to evaluate a large number of devices, this work leverages a novel approach of using a cloud-based Arm device testbed for architectural and security research on timing channels and runs the benchmarks on 34 different physical devices. In parallel, there has been much interest in secure caches to defend the various attacks. Consequently, this paper also investigates secure cache architectures using proposed benchmarks. Especially, this paper implements and evaluates secure PL and RF caches, showing the security of PL and RF caches, but also uncovers new weaknesses.

The railway industry—traditionally a conservative industry with low adaption speed for innovation—is currently entering its digitization phase. The sector faces a challenge in integrating new technologies and approaches into the employed—often safety-critical—systems. Keeping the systems secure while conforming to the demanding safety norms creates previously unknown problems. In the last decades, the number of attacks on the railway system has increased. Furthermore, with standardized digital technologies, the attack surface will keep growing. Therefore, in this work, we look into the foreseeable future of the railway system and present 21 likely use cases. We analyze these use cases regarding possible threats, rate the severity of these threats, and deduce and rate necessary countermeasures. To this end, we model these use cases and the corresponding threats and countermeasures using Attack Graphs. We use a graphical solution for the risk and security analysis due to advantages over other methods, i.e., table-based solutions, like simplified presentation and an easier understanding of relationships, dependencies, and interactions between various elements. From these Attack Graphs, we extracted 14 commonly recurring attack strategies. After analyzing 49 countermeasures regarding their current maturity and further research and standardization demands, we identified 21 in need of further investigation. This implies that 21 necessary countermeasures to secure these future use cases require further research to apply to railway systems or require standardization. These results will help researchers focus on the necessary research and standardization and railway operators to ensure the security of their systems.

In this article, we introduce a novel approach to achieving lightweight device authentication through the use of a low-complexity Convolutional Neural Network (CNN). In our work, we improve the False Authentication Rate (FAR) by transforming the standard CNN into a Bayesian CNN (BCNN or BNN). This transformation enables the use of probabilistic modelling techniques, increasing the model’s robustness and its confidence in authentication decisions. Regardless of the model used, clients authenticate with a retention-based Dynamic Random Access Memory Physical Unclonable Function (DRAM PUF) response. Our approach integrates the low computational complexity of the CNN with the intrinsic security characteristics of the DRAM PUF, offering a robust solution for lightweight and secure device authentication.

The Automotive Service-Oriented software Architecture (ASOA) is a framework for the effective design, fast deployment, and efficient maintenance of automotive software architectures, facilitating reliable communication among loosely coupled services. It maintains a global view of functional units, allowing them to be updated, replaced, and reconnected so that road vehicles obtain or lose capabilities. While the ASOA lives up to expectations regarding reliability and real-time behavior, it lacks a security concept and, therefore, cannot be safely deployed in automotive systems. This work presents a security process to protect ASOA-based communication from compromise and prevent passenger harm. We propose annotating dataflows with security attributes in a web-based architecture design tool and introducing a central component responsible for converting the communication model into tokens securely distributed to ASOA services. They enable secure communication among components while preserving the desired loose coupling, a critical factor for cost-effective system maintainability. Moreover, we support shielded and technically constrained devices in a heterogeneous domain or zonal vehicle network. After formally verifying our token distribution protocol, we evaluate our security process in a self-driving vehicle and prove that it imposes only negligible overhead during operation.