The increasing integration of digital technologies in connected vehicles introduces cybersecurity risks that extend beyond individual vehicles, with the potential to disrupt entire transportation systems. Current practice (e.g., ISO/SAE 21434 TARA) focuses on threat identification and qualitative impact ratings at the vehicle boundary, with limited systemic quantification. This study presents a systematic, simulation-based methodology for quantifying the systemic operational and safety impacts of cyber threats on connected vehicles, evaluating cascading effects across the transport network. Three representative scenarios are examined: (I) telematics-induced sudden braking causing a cascading collision, (II) remote disabling on a motorway (M25) segment, and (III) a compromised Roadside Unit (RSU) spoofing Variable Speed Limit (VSL) and phantom lane closure messages to connected and automated vehicles (CAVs). The results highlight the potential for cascading safety incidents and systemic operational degradation, as evidenced by the defined systemic operational and safety vectors, factors that are insufficiently addressed in the current scope of the ISO/SAE 21434 standard, which primarily focuses on individual vehicle-level threats. The findings underscore the need to incorporate systemic evaluation into existing frameworks to enhance cyber resilience across connected vehicle ecosystems. The framework complements ISO/SAE 21434 by supplying quantitative, reproducible evidence for the impact rating step at a systemic scale, reducing assessor subjectivity and supporting policy and operations, enabling more data-driven evaluations of systemic cyber risks.

The Road Vehicles Cybersecurity Engineering Standard, ISO/SAE 21434, provides a framework for road vehicle Threat Analysis and Risk Assessment (TARA). The TARA framework must include Connected Vehicles (CVs) and their connectivity with external interfaces. However, assessing cyber-attack feasibility on CVs is a significant challenge, as traditionally, qualitative and subjective expert opinions are the norm. Additionally, there is a need for historical data on security-related incidents and dynamically evolving interconnected vehicle-to-everything (V2X) entities for feasibility assessment, which is not readily available. To address this problem, this paper presents, to the best of our knowledge, the first simulation-based TARA framework designed to characterise, quantify, and assess the Window of Opportunity (WO) for attackers—a metric that indicates the likelihood of an attack. A case study involving Bluetooth, with one attacker and one target, is modelled to demonstrate the proposed framework WOLVES’s applicability. Two scenarios have been investigated using different motorway roads in the UK. The primary outcome is the WOLVES framework, which employs a data-driven approach using both prior and likelihood information to estimate the probability of a successful cyber attack on a given technology in CVs. The findings from this research could assist threat analysts, decision-makers, and planners involved in CV risk assessment by enhancing the modelling of attack feasibility for cybersecurity threats in dynamic scenarios and developing appropriate mitigation strategies.

Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript.