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.

Despite the focus on improving cybersecurity awareness, the number of cyberattacks has increased significantly, leading to huge financial losses, with their risks spreading throughout the world. This is due to the techniques deployed in cyberattacks that mainly aim at exploiting humans, the weakest link in any defence system. The existing literature on human factors in phishing attacks is limited and does not live up to the witnessed advances in phishing attacks, which have become exponentially more dangerous with the introduction of generative artificial intelligence (GenAI). This paper studies the implications of AI advancement, specifically the exploitation of GenAI and human factors in phishing attacks. We conduct a systematic literature review to study different human factors exploited in phishing attacks, potential solutions and preventive measures, and the complexity introduced by GenAI-driven phishing attacks. This paper aims to address the gap in the research by providing a deeper understanding of the evolving landscape of phishing attacks with the application of GenAI and associated human implications, thereby contributing to the field of knowledge to defend against phishing attacks by creating secure digital interactions.

The increasing adoption of microservices in software development requires effective recommendation systems that guide developers to relevant microservices. In this paper, we introduce MicroRec, a novel microservice recommender framework which leverages insights from Stack Overflow posts and the power of Large Language Models (LLMs). MicroRec utilizes a dual-encoder architecture that combines contrastive learning and semantic similarity learning, allowing us to achieve robust and accurate retrieval and ranking of relevant posts based on user queries. Using LLMs, MicroRec builds up a deep understanding of both user queries and microservices through the information they provide (e.g., README files and Dockerfiles). Our empirical evaluations demonstrate significant improvements brought by MicroRec over the existing methods across a variety of performance metrics including MRR, MAP, and precision@k. In addition, the results returned by MicroRec were fourteen times more accurate than those provided by the existing recommendation tool on the widely-used Docker Hub platform.

Software-defined networking (SDN) is an innovative technology that has the potential to enhance the scalability, flexibility, and security of telecommunications networks. The emergence and development of SDNs have introduced new opportunities and challenges in the telecommunications industry. One of the major challenges encountered by SDNs is the timing side-channel attacks. These attacks exploit timing information to expose sensitive data, including flow tables, routes, controller types, and ports, which pose a significant threat to communication networks. Existing techniques for mitigating timing side-channel attacks primarily focus on limiting them via network architectural changes. This significantly increases the overhead of SDNs and makes it difficult to identify the origin of the attack. To secure resilient integration of SDN in telecommunications networks, it is necessary to conduct comprehensive research that not only identifies the attack activity, but also formulates an adequate response. In this paper, we propose a detection and response solution for timing side-channel attacks in SDN. We used a machine learning-based approach to detect the probing activity and identify the source. To address the identified timing side-channel attack queries, we propose a response mechanism. This entails devising a feedback-oriented response to counter the identified source, such as blocking or diverting it, while minimising any adverse effects on legitimate network traffic. This methodology is characterised by an automated data-driven approach that enables prompt and effective responses. The architecture of this security solution ensures that it has a minimal impact on network traffic and resource usage as it is designed to be used in conjunction with SDN. The overall design findings show that our detection approach is 94% precise in identifying timing side-channel attacks in SDN when compared with traditional mitigation strategies. Additionally, the response mechanism employed by this approach yielded highly customised and precise responses, resulting in an impressive accuracy score of 97.6%.

Aligning strategic objectives with business services is important for an organisation's long-term success. The automation of those alignments offers significant benefits in terms of identifying strategies that are not supported by any services, or services that are irrelevant to the organisation's strategies. Automated strategy-service alignment also enables organizations to proactively respond to changes in complex environments and allocate resources effectively to increase the likelihood of achieving strategic objectives. StraAlgin offers a methodology and tool for defining, modeling and refining business objectives and strategies, and aligning them with business services. It also enables automated identification of the optimal choice of services that could realize organizational strategies. Evaluation results have demonstrated a high level accuracy of our approach and its scalability to a large number of strategies and services. This paper proposes an approach to automate the assessment and alignment of services and strategies. Our approach is implemented in the form of StraAlign toolkit. StraAlign provides the necessary vocabulary for business objective description and offers automated support to improve the evaluation and implementation of strategic service alignments.