Blockchain interoperability is an innovative feature of blockchain technology that is rapidly gaining momentum in various fields. The mass adoption of enterprise blockchains has not yet been achieved because blockchain networks act as isolated islands that cannot connect or exchange assets and/or information. In addition, the invocation of smart contracts is restricted only to on-chain executions because of the lack of connectivity between the blockchains. This survey paper aims to conduct a comprehensive and thorough literature review regarding blockchain interoperability, with special highlight on blockchain Oracles being state-of-the-art. Oracles have shown potential as an emerging technology that has revolutionized the blockchain ecosystem by acting as agents that fetch external information into the blockchain ecosystem. A detailed comparative discussion of different blockchain interoperability techniques is presented, showing the strengths and weaknesses of each technique. Being overlooked in the literature, the shortcomings of these techniques in comparison to Oracles are identified, addressing how Oracles have succeeded in overcoming many of these limitations. In addition, the literature lacks a focus on the latest market solutions adopting blockchain Oracles, and only a few studies have considered them in detail. This gap has been addressed through an in-depth assessment of the latest market solutions adopting Oracles in the past few years. Finally, design issues trying to achieve the best practices of Oracles, future directions, and identified research gaps are highlighted.

The rapid evolution of technologies such as the Internet of Things (IoT), 5G, and cloud computing has exponentially increased the complexity of cyber attacks. Modern Intrusion Detection Systems (IDSs) must be capable of identifying not only frequent, well-known attacks but also low-frequency, subtle intrusions that are often missed by traditional systems. The challenge is further compounded by the fact that most IDS rely on black-box machine learning (ML) and deep learning (DL) models, making it difficult for security teams to interpret their decisions. This lack of transparency is particularly problematic in environments where quick and informed responses are crucial. To address these challenges, we introduce the XI2S-IDS framework—an Explainable, Intelligent 2-Stage Intrusion Detection System. The XI2S-IDS framework uniquely combines a two-stage approach with SHAP-based explanations, offering improved detection and interpretability for low-frequency attacks. Binary classification is conducted in the first stage followed by multi-class classification in the second stage. By leveraging SHAP values, XI2S-IDS enhances transparency in decision-making, allowing security analysts to gain clear insights into feature importance and the model’s rationale. Experiments conducted on the UNSW-NB15 and CICIDS2017 datasets demonstrate significant improvements in detection performance, with a notable reduction in false negative rates for low-frequency attacks, while maintaining high precision, recall, and F1-scores.

As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.

Remote Image-based rendering (IBR) is an effective solution for rendering complex 3D scenes on mobile devices. A server renders the 3D scene and streams the rendered images to the client. However, sending a large number of images is inefficient due to the possible bandwidth limitations of wireless connections. In this paper, we propose a server-side prefetching scheme that predicts client movements and hence prefetches the corresponding images. In addition, an event driven simulator was designed and implemented to evaluate the performance of the proposed scheme. The simulator was used to compare between prediction-based prefetching and prefetching images based on spatial locality. Several experiments were conducted to study the performance using different movement patterns and different virtual environments (VEs). The results have shown that the hit ratio of the prediction-based scheme is greater than the localization scheme in the case of random and circular walk movement patterns by approximately 35% and 17%, respectively. For a VE with high level of details, the proposed scheme outperforms the localization scheme by approximately 13%. However, for a VE with low level of details, the localization based scheme outperforms the proposed scheme by only 5%.

Interaction interoperability should allow any input device and related interaction technique to accomplish tasks afforded by an application. We propose a framework to support such interoperability. The framework provides ontologies of input devices and interaction techniques/tasks. We focus on interaction interoperability in virtual environments (VEs) where users have different software/hardware platforms. The main benefit is the ability to use ontologies to define tasks needed to be done in a VE without specifying how these tasks should be accomplished. Based on the user and application profiles, we can dynamically select input devices and assign interaction techniques to defined tasks.