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 automotive industry increasingly relies on Over-the-Air (OTA) updates to deliver essential security patches to vehicles. However, this dependence may introduce significant cy-bersecurity vulnerabilities, particularly concerning the integrity and privacy of updates. This paper presents an integrated framework that combines threat modeling and formal verification by employing an identical system model across all stages. The process begins with applying the ThreatGet tool to identify potential threats in the OTA update process, which directly guide the formulation of formal security requirements expressed as Computation Tree Logic (CTL) properties. The same high-level model encompassing a Cloud Server (CS), Telematics Control Unit (TCU), Central Gateway Unit (CGU), Advanced Driver Assistance System (ADAS) module, and an attacker module is consistently used for both threat modeling and encoding in the NuSMV model checker. This unified approach ensures that identified threats translate seamlessly into verifiable properties. Experimental threat analysis and verification results demonstrate the effectiveness of our integrated approach in uncovering OTA update vulnerabilities properties.

The rise in Connected and Automated Vehicles (CAVs) and Intelligent Transport Systems (ITSs) introduced by OEMs has increased the demand for modern vehicle sophistication. This sophistication involves a variety of software capabilities and functionalities embedded in over 100 ECUs in a vehicle. This has led to the need for over-the-air (OTA) updates. OTA updates can be delivered wirelessly, eliminating the need to bring vehicles to the garage for updates. This is more convenient for owners, reduces costs for OEMs, and reduces greenhouse gas emissions. There exist different OTA update considerations that are adopted by automotive OEMs, such as the Uptane framework, Open Mobile Alliance Device Management (OMA-DM) standard, and the general ISO 24089 standard, including subvariance of Uptane and OMA-DM. However, the systematic implementation of security-by-design applying ISO 21434 in OTA systems is less employed, and there remains a gap in this practice of security-by-design that the automotive industry can adapt to ensure a systematic approach to secure OTA update technology. OTA update security hinges on identifying vulnerability pathways for potential malicious attacks. Therefore, identifying and mitigating potential vulnerabilities throughout the OTA update process is critical for robust security. This paper proposes an adaptable security-by-design approach to OTA update, built and extended from our work Iyieke et al. (2023). The adaptable security-by-design approach is then applied to a developed prototype OTA update system based on the Uptane framework as implemented by Toradex. Security-by-design is a well-established concept in enterprise systems, but is still developing in the cyber–physical system of automotive cybersecurity. Our proposed approach covers the security engineering lifecycle, the logical security layered concept, and the security architecture. A threat analysis and risk assessment (TARA) is performed based on the international automotive cybersecurity standard ISO/SAE 21434. The highest threats identified from the TARA are formalized, and corresponding mitigation actions are defined according to UNECE WP29. Penetration testing is conducted to verify the approach’s capability to reinforce the security of the OTA update systems against some of the identified risks and threats. Our proposed approach provides a systematic and adaptable security-by-design approach to ensure secure OTA updates in modern vehicles; OEMs and other stakeholders can use it to develop secure OTA systems regardless of the OTA update technology used.

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.

Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic congestion control, location-aware advertisements and monitoring public health and well-being. Recent developments in smartphone and location sensors technology and the prevalence of using location-based social networks alongside the improvements in AI and machine learning techniques provide an excellent opportunity to exploit massive amounts of historical and real-time contextual information to recognise mobility patterns and achieve more accurate and intelligent predictions. This unique survey provides a comprehensive overview of the next useful location prediction problem with context-awareness and the related studies. First, we explain the concepts of context and context-awareness and define the next location prediction problem. Then we analyse more than 30 studies in this field concerning the prediction method, the challenges addressed, the datasets and metrics used for training and evaluating the model and the types of context incorporated. Finally, we discuss the advantages and disadvantages of different approaches, focusing on the usefulness of the predicted location and identifying the open challenges and future work on this subject.

Summary We present a framework for the modelling, specification, and verification of ontology‐driven multi‐agent rule‐based systems (MASs). We assume that each agent executes in a separate process and that they communicate via message passing. The proposed approach makes use of abstract specifications to model the behaviour of some of the agents in the system and exploits information about the reasoning strategy adopted by the agents. Abstract specifications are given as Linear Temporal Logic (LTL) formulas which describe the external behaviour of the agents, allowing their temporal behaviour to be compactly modelled. Both abstraction and strategy have been combined in an automated model checking encoding tool Tovrba for rule‐based multi‐agent systems which allows the system designer to specify information about agents' interaction, behaviour, and execution strategy at different levels of abstraction. The Tovrba tool generates an encoding of the system for the Maude LTL model checker, allowing properties of the system to be verified.

Resource-bounded alternating-time temporal logic (RB-ATL), an extension of Coalition Logic (CL) and Alternating-time Temporal Logic (ATL), allows reasoning about resource requirements of coalitions in concurrent systems. However, many real-world systems are inherently probabilistic as well as resource-bounded, and there is no straightforward way of reasoning about their unpredictable behaviours. In this paper, we propose a logic for reasoning about coalitional power under resource constraints in the probabilistic setting. We extend RB-ATL with probabilistic reasoning and provide a standard algorithm for the model-checking problem of the resulting logic Probabilistic Resource-Bounded ATL (pRB-ATL).

Abstract Over the last few years, context-aware computing has received a growing amount of attention among the researchers in the IoT and ubiquitous computing community. In principle, context-aware computing transforms a physical environment into a smart space by sensing the surrounding environment and interpreting the situation of the user. This process involves three major steps: context acquisition, context modelling, and context-aware reasoning. Among other approaches, ontology-based context modelling and rule-based context reasoning are widely used techniques to enable semantic interoperability and interpreting user situations. However, implementing rich context-aware applications that perform reasoning on resource-bounded mobile devices is quite challenging. In this paper, we present a context-aware systems development framework for smart spaces, which includes a lightweight efficient rule engine and a wide range of user preferences to reduce the number of rules while inferring personalized contexts. This shows rules can be reduced in order to optimize the inference engine execution speed, and ultimately to reduce total execution time and execution cost.

Many problems in AI and multi-agent systems research are most naturally formulated in terms of the abilities of a coalition of agents. There exist several excellent logical tools for reasoning about coalitional ability. However, coalitional ability can be affected by the availability of resources, and there is no straightforward way of reasoning about resource requirements in logics such as Coalition Logic (CL) and Alternating-time Temporal Logic (ATL). In this article, we describe a logic for reasoning about coalitional ability under resource constraints. We extend ATL with costs of actions and hence of strategies. We give a complete and sound axiomatization of the resulting logic, Resource-Bounded ATL (RB-ATL) and a model-checking algorithm for it.

This paper presents a conceptual framework and multi-agent model for context-aware decision support in dynamic smart environments based on heterogeneous knowledge sources. A Protégé plug-in for rules extraction from distributed ontologies has been developed, which allows us to model context-aware agents using the notion of multi-context systems. Extracted rules can be annotated to match the users’ needs and to develop a preference model to support their preferences so as to provide a user with a more personalized services. The use of the proposed framework is illustrated using a simple fact-based preference model developed from ontologies considering two different smart environment domains.

This paper complements our previous work on formal modeling of resource-bounded context-aware systems, which handle inconsistent context information using defeasible reasoning, by focusing on automated analysis and verification. A case study demonstrates how model checking techniques can be used to formally analyze quantitative and qualitative properties of a context-aware system based on message passing among agents. The behavior (semantics) of the system is modeled by a term rewriting system and the desired properties are expressed as LTL formulas. The Maude LTL model checker is used to perform automated analysis of the system and verify non-conflicting context information guarantees it provides.

Recent work on Alternating-Time Temporal Logic and Coalition Logic has allowed the expression of many interesting properties of coalitions and strategies. However, there is no natural way of expressing resource requirements in these logics. In this article, we present a Resource-Bounded Coalition Logic (RBCL) that has explicit representation of resource bounds in the language. We give a complete and sound axiomatization of RBCL, a procedure for deciding satisfiability of RBCL formulas, and a model-checking algorithm.