The popularity of the Metaverse has rapidly increased in recent years. However, despite the attention, investment, and promise of the Metaverse, there are various cybersecurity issues that must be addressed before the Metaverse can truly be adopted in practice for serious applications. The realization of the Metaverse is envisioned by many as requiring the use of visualization technologies such as Virtual Reality (VR) and Augmented Reality (AR). This visual aspect of the Metaverse will undoubtedly give rise to emerging cybersecurity threats that have not received much attention. As such, the purpose of this survey is to investigate cybersecurity threats faced by the Metaverse in relation to visualization technologies. Furthermore, this paper discusses existing work and open research directions on the development of countermeasures against such threats. As the Metaverse is a multidisciplinary topic, the intention of this work is to provide a background of the field to aid researchers in related areas.

Sharing data securely and efficiently has been identified as an issue in IoT-based smart systems such as smart cities, smart agriculture, smart health, etc. A large number of IoT devices are used in these smart systems and they produce a large amount of data. IoT devices generally have limited storage and processing capabilities, and configuring any security techniques on these devices is a challenge. In this paper, we propose a novel device identity management approach for blockchain-based IoT systems that provides data security in two ways. Firstly, a lightweight time-based identification protocol that uses hub identification for validating data. Secondly, data storage is augmented with an effective blockchain application for providing easy access and immutability for data sharing among multiple parties. Our initial prototype implementation shows that: our identity management approach can be implemented in large scale settings, our system can be effectively implemented in blockchain platforms, and our performance evaluation result shows that the prototype fulfills system requirements adequately.

The rapid growth of the Internet of Things (IoT) has exposed billions of interconnected, heterogeneous, and resource-constrained devices to increasingly sophisticated threats. To evaluate the readiness of current intrusion detection systems (IDSs), this study reviews 32 recent IoT-IDS proposals spanning conventional, machine-learning, deep-learning, and hybrid approaches. Each system is assessed against 10 criteria that reflect practical IoT requirements, including real-time performance, latency, lightweight design, detection accuracy, mitigation capabilities, integrated detection-and-mitigation workflows, adaptability, resilience to advanced attacks, validation in realistic environments, and scalability. The results indicate that although many approaches achieve high detection accuracy, most do not meet real-time and lightweight thresholds commonly cited in IoT deployment literature. Mitigation features are often absent, adaptability is rarely implemented, and 29 out of 32 studies rely solely on offline datasets, thereby limiting confidence in their robustness to deployment. Scalability remains the most significant limitation, as none of the reviewed IDSs have tested their performance under realistic multi-node or high-traffic conditions, even though scalability is critical for large IoT ecosystems. Overall, the review suggests that future IoT IDS research should move beyond accuracy-focused models and toward lightweight, adaptive, and autonomous solutions that incorporate mitigation, support real-time inference, and undergo standardized evaluations under real-world operating conditions.

Trust management systems (TMSs) play an important role in Internet of Things (IoT) by providing a means of finding whether a given device can provide a service to a satisfactory level, and for identifying potentially malicious devices in the network. Context awareness extends trust models by allowing a trustor to filter and aggregate evidence by their relevance to the current situation. Context awareness is important in the formulation of trust in IoT networks due to their heterogeneity and due to the dynamic changes in the capabilities of IoT devices. In this article, we have proposed a new type of attack on context-aware trust models for IoT systems, context-based attacks. In this attack, an adversary manipulates the context to impact a target group of IoT devices, while other devices in nontargeted groups are not even aware of the attack. We have demonstrated the effectiveness of this new type of attack on seven previously proposed trust models through practical simulations and theoretical proofs. This article also proposes a new TMS that can mitigate such context-based attacks.

With the growing use of mobile devices, location-based services (LBS) are becoming increasingly popular. BLS deliver accurate services to individuals according to their geographical locations, but privacy issues have been the primary concerns of users. Privacy-preserving LBS (PPLBS) were proposed to protect location privacy, but there are still some problems: (1) a semi-trusted third party (STTP) is required to blur users’ locations; (2) both the computation and communication costs of generating a query are linear with the size of queried areas; (3) the schemes were not formally treated, in terms of definition, security model, security proof, etc . In this paper, to protect location privacy and improve query efficiency, an oblivious location-based services (OLBS) scheme is proposed. Our scheme captures the following features: (1) an STTP is not required; (2) users can query services without revealing their exact location information; (3) the service provider only knows the size of queried areas and nothing else; and (4) both the computation and communication costs of generating a query is constant, instead of linear with the size of queried areas. We formalise both the definition and security model of our OLBS scheme, and propose a concrete construction. Furthermore, the implementation is conducted to show its efficiency. The security of our scheme is reduced to well-known complexity assumptions. The novelty is to reduce the computation and communication costs of generating a query and enable the service provider to obliviously generate decrypt keys for queried services. This contributes to the growing work of formalising PPLBS schemes and improving query efficiency.