Project BeARCAT : Baselining, Automation and Response for CAV Testbed Cyber Security : Connected Vehicle & Infrastructure Security Assessment

Connected, software-based systems are a driver in advancing the technology of transportation systems. Advanced automated and autonomous vehicles, together with electrification, will help reduce congestion, accidents and emissions. Meanwhile, vehicle manufacturers see advanced technology as enhancing their products in a competitive market. However, as many decades of using home and enterprise computer systems have shown, connectivity allows a system to become a target for criminal intentions. Cyber-based threats to any system are a problem; in transportation, there is the added safety implication of dealing with moving vehicles and the passengers within

Affected vehicle population in automotive cyber risk assessments

The computerised and connected car brings with it the possibility of cyber-attacks. The automotive industry is addressing the cyber threat with new regulations and standards. As a result, cyber risk assessments will become part of the systems engineering process as vehicle manufacturers build cyber resilience and trust into their products. This work examines an overlooked aspect of automotive cyber threats, that of the affected vehicle population as a risk rating impact factor. It examines real-world attacks for a qualitatively affected population and then uses UK vehicle statistics to see if the qualitative population can be related to physical quantities. A vehicle population risk rating impact factor was derived from the real-world UK vehicle data; however, limitations exist, and further work is required to quantify other vehicle risk assessment impact and likelihood factors

Incorporating alternative interaction modes, forbidden links and trait‐based mechanisms increases the minimum trait dimensionality of ecological networks

1. Individual-level traits mediate interaction outcomes and community structure. It is important, therefore, to identify the minimum number of traits that characterise ecological networks, that is, their ‘minimum dimensionality’. Existing methods for estimating minimum dimensionality often lack three features associated with in- creased trait numbers: alternative interaction modes (e.g. feeding strategies such as active vs. sit-and-wait feeding), trait-mediated ‘forbidden links’ and a mechanistic description of interactions. Omitting these features can underestimate the trait numbers involved, and therefore, minimum dimensionality. We develop a ‘mini- mum mechanistic dimensionality’ measure, accounting for these three features.2. The only input our method requires is the network of interaction outcomes. We assume how traits are mechanistically involved in alternative interaction modes. These unidentified traits are contrasted using pairwise performance inequalities between interacting species. For example, if a predator feeds upon a prey spe- cies via a typical predation mode, in each step of the predation sequence, the predator's performance must be greater than the prey's. We construct a system of inequalities from all observed outcomes, which we attempt to solve with mixed integer linear programming. The number of traits required for a feasible system of inequalities provides our minimum dimensionality estimate.3. We applied our method to 658 published empirical ecological networks includ- ing primary consumption, predator–prey, parasitism, pollination, seed dispersal and animal dominance networks, to compare with minimum dimensionality estimates when the three focal features are missing. Minimum dimensionality was typically higher when including alternative interaction modes (54% of empirical networks), ‘forbidden interactions’ as trait-mediated interaction outcomes (92%) or a mechanistic perspective (81%), compared to estimates missing these features. Additionally, we tested minimum dimensionality estimates on simulated networks with known dimensionality. Our method typically estimated a higher minimum dimensionality, closer to the actual dimensionality, while avoiding the overestimation associated with a previous method.4. Our method can reduce the risk of omitting traits involved in different interaction modes, in failure outcomes or mechanistically. More accurate estimates will allow us to parameterise models of theoretical networks with more realistic structure at the interaction outcome level. Thus, we hope our method can improve predictions of community structure and structure-dependent dynamics

The depressogenic potential of added dietary sugars

Added sugars are ubiquitous in contemporary Western diets. Although excessive sugar consumption is now robustly associated with an array of adverse health consequences, comparatively little research has thus far addressed its impact on the risk of mental illness. But ample evidence suggests that high-dose sugar intake can perturb numerous metabolic, inflammatory, and neurobiological processes. Many such effects are of particular relevance to the onset and maintenance of depressive illness, among them: systemic inflammation, gut microbiota disruption, perturbed dopaminergic reward signaling, insulin resistance, oxidative stress, and the generation of toxic advanced glycation end-products (AGEs). Accordingly, we hypothesize that added dietary sugars carry the potential to increase vulnerability to major depressive disorder, particularly at high levels of consumption. The present paper: (a) summarizes the existing experimental and epidemiological research regarding sugar consumption and depression vulnerability; (b) examines the impact of sugar ingestion on known depressogenic physiological processes; and (c) outlines the clinical and theoretical implications of the apparent sugar-depression link. We conclude that the extant literature supports the hypothesized depressogenic impact of added dietary sugars, and propose that an improved understanding of the effects of sugar on body and mind may aid in the development of novel therapeutic and preventative measures for depression

Netrin Signaling Breaks the Equivalence between Two Identified Zebrafish Motoneurons Revealing a New Role of Intermediate Targets

We previously showed that equivalence between two identified zebrafish motoneurons is broken by interactions with identified muscle fibers that act as an intermediate target for the axons of these motoneurons. Here we investigate the molecular basis of the signaling interaction between the intermediate target and the motoneurons.We provide evidence that Netrin 1a is an intermediate target-derived signal that causes two equivalent motoneurons to adopt distinct fates. We show that although these two motoneurons express the same Netrin receptors, their axons respond differently to Netrin 1a encountered at the intermediate target. Furthermore, we demonstrate that when Netrin 1a is knocked down, more distal intermediate targets that express other Netrins can also function to break equivalence between these motoneurons.Our results suggest a new role for intermediate targets in breaking neuronal equivalence. The data we present reveal that signals encountered during axon pathfinding can cause equivalent neurons to adopt distinct fates. Such signals may be key in diversifying a neuronal population and leading to correct circuit formation

Vehicular over-the-air software upgrade threat modelling

The major vehicle manufacturers deploy Over-the-Air (OTA) software update technology for their vehicle systems. In this research, we review the literature on the cybersecurity of the OTA software update mechanism. This allowed the derivation of a high-level reference architecture for the OTA system. The architecture and review guided the analysis of the OTA system attack surface. A novel asset-centric threat modelling method is derived from the analysis and applied to the OTA software upgrade use case. System assets identification, system decomposition and labelling are three steps of a four-step threat modelling methodology. The final step enables attack vector threat analysis and mitigation. The final contribution comes from actionable cybersecurity recommendations for software upgrade systems, providing threat mitigation recommendations for their secure implementation. Knowledge of potential long-range wireless attacks and other OTA system threats provides a foundation for stakeholders’ strategic investment in cybersecurity risk reduction. This investment is needed to address a dilemma. On the one hand, OTA systems are a useful technology for updating the software in cyber-physical systems, however, they do provide a potential conduit for cyber attacks. Whilst this work researched vehicular OTA systems, it could be applied to other cyber-physical systems that require secure software updates over a lifecycle

A practical implementation of quantum-derived keys for secure vehicle-to-infrastructure communications

We provide a practical implementation of a free space optical quantum key distribution (FSO-QKD) system within a vehicle-to-infrastructure (V2I) application developed under the Innovate UK AirQKD project. The FSO-QKD system provides the quantum secure encryption keys that serve as the foundation for secure communications throughout the V2I application to address known concerns over V2I security. This document includes summaries of the quantum key generation process and the deployed V2I technology. Subsequently, a high-level view of the system design, the practical experiment, and its execution are presented. Multiple AirQKD project partners developed technologies ranging from semiconductors and hardware to security protocols and software, to enable the QKD-secured V2I system. The developed technology includes a novel zero-trust security protocol used to protect the V2I communications, ensuring that spoofed V2I messages from a compromised device are not accepted by the system