Security Operations Centres (SOCs) face sustained pressure from alert fatigue, fragmented tooling, analyst cognitive overload, and increasingly complex multi-stage attack campaigns. Although Artificial Intelligence (AI) and Machine Learning (ML) can support detection, triage, and response, their operational value is constrained when trust, transparency, accountability, and human oversight are not systematically addressed. This paper presents the Adaptive Trust-Aware SOC Human–AI Teaming (ATA-SOC-HAT) framework, a design-oriented conceptual framework for trust-calibrated collaboration between SOC analysts and AI services. The framework was derived from a structured literature review and maps recurrent barriers in SOC human–AI collaboration to explicit functional modules, including explainable interaction, dynamic task allocation, trust calibration, arbitration, and continuous learning. We describe the framework architecture and workflow and illustrate its intended operation through an advanced threat scenario. Rather than claiming empirical validation, the paper provides a prioritised evaluation roadmap that identifies a practical core set of metrics for future practitioner-in-the-loop studies, simulations, and prototype implementations. We also summarise how the framework aligns with relevant cybersecurity and trustworthy-AI guidance to support accountable deployment. The contribution of this work is therefore a traceable, literature-derived conceptual framework and a realistic basis for future empirical evaluation of human–AI teaming in SOC environments.

Additive Manufacturing (AM) is transforming industries by enabling rapid prototyping and customised production. However, as AM processes become increasingly digitised and interconnected, they introduce significant cybersecurity vulnerabilities, including intellectual property theft, design manipulation, and counterfeit production. This paper offers a comprehensive analysis of cyber and cyber–physical threats within the AM supply chain, addressing a critical research gap that has largely focused on isolated security aspects. Building upon existing taxonomies, we expand cybersecurity frameworks to incorporate emerging AM-specific threats. We propose a structured attack taxonomy that categorises threats by attacker goals, targets, and methods, supported by real-world case studies. The paper emphasizes the need for robust cybersecurity measures to protect intellectual property, ensure production integrity, and strengthen supply chain security. Finally, we present mitigation strategies to counter these threats, laying the foundation for future research and best practices to secure AM ecosystems.

Advancements in 6G communication technology hold great promise for Vehicular Ad Hoc Networks (VANETs), providing critical real-time data to vehicles with low latency, high speed, and enhanced network capacity. While 6G communications significantly improve transportation efficiency, they also introduce significant challenges in securing communication. Several studies have addressed anonymous authentication and key exchange protocols for 6G-enabled VANETs, including the work (doi.org/10.1109/TITS.2021.3099488) by Vijayakumar et al. on Anonymous Batch Authentication and Key Exchange Protocols. Although this protocol is claimed to be secure, our analysis identifies critical vulnerabilities, including susceptibility to man-in-the-middle attacks, impersonation, and unauthorized access. This paper proposes a secure and anonymous batch authentication and key exchange protocol for 6G-enabled VANETs to address this challenge. The formal security analysis demonstrates that the proposed solution effectively mitigates man-in-the-middle attacks, impersonation, and unauthorized access. Additionally, performance evaluations show that the proposed scheme is practically feasible.

The potential of the Internet-of-Medical-Things (IoMT) technology for interconnecting the biomedical sensors in e-health has ameliorated the people's living standards. Another technology recognized in the recent e-healthcare is outsourcing the medical data to the cloud. There are, however, several stipulations for adopting these two technologies. The most difficult is the privacy of medical data and the challenge resulting from the resource constraint environment of sensor devices. In this article, we present the state-of-the-art secure and efficient cloud-centric IoMT-enabled smart healthcare system with public verifiability. The system novelty implements an escrow-free identity-based aggregate signcryption (EF-IDASC) scheme to secure data transmission, which is also proposed in this article. The proposed smart healthcare system fetches the medical data from multiple sensors implanted on the patient's body, signcrypts and aggregates them under the proposed EF-IDASC scheme, and outsources the data on the medical cloud server via smartphone. The system does not reveal any information about the identity and medical data of the patient. We further analyze the performance of the proposed smart healthcare system in terms of energy consumption. Moreover, we compare the performance of the proposed EF-IDASC scheme with other related schemes.