The rapid increase in electric vehicles (EVs) and the widespread deployment of charging stations have made secure authentication a critical requirement in Vehicle-to-Grid (V2G) environments. The growing interconnection among EVs, charging stations, and grid infrastructure introduces serious security and privacy challenges, including impersonation, replay, ephemeral secret leakage, and physical tampering attacks. Although several authentication protocols have been proposed for EV charging services, many existing schemes lack robust tamper-resistant and location-aware authentication capabilities and remain unsuitable for dynamic and resource-constrained V2G conditions. To address these limitations, this paper proposes a robust, tamper-resistant, and location-sensitive authentication protocol for securing EV charging services in V2G environments. The proposed protocol integrates configurable Arbiter Physical Unclonable Functions (A-PUFs) to provide device-level protection against physical tampering and unauthorized charging access. It also employs lightweight cryptographic primitives and techniques, including one-way hash functions, XOR operations, concatenation operations, and timestamp-based freshness verification, to support efficient mutual authentication and secure session key agreement. The security of the proposed protocol is evaluated through informal analysis and formal verification under the Random Oracle Model (ROM). Furthermore, comparative analysis demonstrates that the proposed protocol achieves a 17.16% reduction in communication cost and a 7.49% reduction in average computation cost compared with existing authentication protocols while maintaining strong security features. The results confirm that the proposed scheme enhances the security, efficiency, and practical deployability of EV charging authentication for next-generation V2G networks.

Stakeholders' security decisions play a fundamental role in determining security requirements, yet, little is currently understood about how different stakeholder groups within an organisation approach security and the drivers and tacit biases underpinning their decisions. We studied and contrasted the security decisions of three demographics-security experts, computer scientists and managers-when playing a tabletop game that we designed and developed. The game tasks players with managing the security of a cyber-physical environment while facing various threats. Analysis of 12 groups of players (4 groups in each of our demographics) reveals strategies that repeat in particular demographics, e.g., managers and security experts generally favoring technological solutions over personnel training, which computer scientists preferred. Surprisingly, security experts were not ipso facto better players-in some cases, they made very questionable decisions-yet they showed a higher level of confidence in themselves. We classified players' decision-making processes, i.e., procedure-, experience-, scenario- or intuition-driven. We identified decision patterns, both good practices and typical errors and pitfalls. Our game provides a requirements sandbox in which players can experiment with security risks, learn about decision-making and its consequences, and reflect on their own perception of security.

Motivation: The security of any system is a direct consequence of stakeholders' decisions regarding security requirements. Such decisions are taken with varying degrees of expertise, and little is currently understood about how various demographics - security experts, general computer scientists, managers - approach security decisions and the strategies that underpin those decisions. What are the typical decision patterns, the consequences of such patterns and their impact on the security of the system in question? Nor is there any substantial understanding of how the strategies and decision patterns of these different groups contrast. Is security expertise necessarily an advantage when making security decisions in a given context? Answers to these questions are key to understanding the "how" and "why" behind security decision processes.

Security is one of the biggest challenges facing organisations in the modern hyper-connected world. A number of theoretical security models are available that provide best practice security guidelines and are widely utilised as a basis to identify and operationalise security requirements. Such models often capture high-level security concepts (e.g., whitelisting, secure configurations, wireless access control, data recovery, etc.), strategies for operationalising such concepts through specific security controls, and relationships between the various concepts and controls. The threat landscape, however, evolves leading to new tacit knowledge that is embedded in or across a variety of security incidents. These unknown knowns alter, or at least demand reconsideration of the theoretical security models underpinning security requirements. In this paper, we present an approach to discover such unknown knowns through multi-incident analysis. The approach is based on a novel combination of grounded theory and incident fault trees. We demonstrate the effectiveness of the approach through its application to identify revisions to a theoretical security model widely used in industry.