The rapid proliferation of internet of things (IoT) devices has ushered in a new era of technological development. However, this growth has also exposed these devices to various cybersecurity risks... Show More

The rapid proliferation of internet of things (IoT) devices has ushered in a new era of technological development. However, this growth has also exposed these devices to various cybersecurity risks, including command and control (C&C) attacks. C&C attacks involve unauthorized entities taking control of IoT devices to carry out malicious activities. Traditional cybersecurity measures often fall short in addressing these evolving threats. To enhance IoT security and counter C&C threats, this study explores the potential of supervised learning, a subfield of machine learning. Supervised learning, a method that utilizes past data to train machine learning models capable of independently identifying patterns indicative of C&C threats in real time, offers additional protection to IoT networks. This article delves into the advantages and drawbacks of this approach, considering factors such as the need for well-defined labeled datasets, resource constraints of IoT devices, and ethical considerations surrounding data security.

The advent of the metaverse has revolutionized virtual interactions and navigation, introducing intricate access control challenges. This paper addresses the need for effective access control models... Show More

The advent of the metaverse has revolutionized virtual interactions and navigation, introducing intricate access control challenges. This paper addresses the need for effective access control models in the cloud-based metaverse. It explores its distinct characteristics, including its dynamic nature, diverse user base, and shared spaces, highlighting privacy concerns and legal implications. The paper analyzes access control principles specific to the cloud-based metaverse, emphasizing least privilege, separation of duties, RBAC, defense-in-depth, and auditability/accountability. It delves into identity verification and authorization methods, such as biometrics, multi-factor authentication, and role-based/attribute-based authorization. Advanced access control technologies for the cloud-based metaverse are examined, including SSO solutions, blockchain-based access control, ABAC, adaptive access control, and VMI for isolation. Risk mitigation strategies encompass IDS/IPS, SIEM, and user education programs.

The aim of this article proposes an innovative solution for developing a museum-guide system, which employs a voice-activated assistant paired with 3-D hologram displays, that utilizes Amazon web... Show More

The aim of this article proposes an innovative solution for developing a museum-guide system, which employs a voice-activated assistant paired with 3-D hologram displays, that utilizes Amazon web services (AWS) to enhance the visitor experience at the Bahrain National Museum. The proposed system uses software engineering as a service (SaaS) and involves an agile development process model with microservice architecture that adapts cloud computing capabilities to provide scalability, reliability, and maintainability. The proposed system enhances the existing museum infrastructure and databases through a flexible, API-based architecture. The proposed system is highly adaptable and flexible in different desirable aspects of user experience goals. The implementation results proved that the system is highly reliable, adaptable, and efficient and has the potential to improve the user experience by transforming the way museum visitors explore and interact with user interfaces of the museum-guide system.

The identification of ambiguities in Arabic requirement documents plays a crucial role in requirements engineering. This is because the quality of requirements directly impacts the overall success... Show More

The identification of ambiguities in Arabic requirement documents plays a crucial role in requirements engineering. This is because the quality of requirements directly impacts the overall success of software development projects. Traditionally, engineers have used manual methods to evaluate requirement quality, leading to a time-consuming and subjective process that is prone to errors. This study explores the use of machine learning algorithms to automate the assessment of requirements expressed in natural language. The study aims to compare various machine learning algorithms according to their abilities in classifying requirements written in Arabic as decision tree. The findings reveal that random forest outperformed all stemmers, achieving an accuracy of 0.95 without employing a stemmer, 0.99 with the ISRI stemmer, and 0.97 with the Arabic light stemmer. These results highlight the robustness and practicality of the random forest algorithm.

With the advent of internet of things (IoT), new network paradigms have emerged. One such technology is cloudlets. Cloudlets are being increasingly used in various IoT-based applications such as... Show More

With the advent of internet of things (IoT), new network paradigms have emerged. One such technology is cloudlets. Cloudlets are being increasingly used in various IoT-based applications such as smart homes, smart cities, healthcare, and industrial automations. Cloudlets have an advantage of proximity to the end-device while offering services similar to the cloud. Existing cloudlets use IEEE 802.11 for communication between nodes. In this paper, the authors present a protocol customized for usage in cloudlets, which also considers various limitations of the node that constitute the cloudlet. The nodes on the cloudlet are generally constrained in terms of power and memory when compared to nodes on a cloud. The custom protocol also incorporates fault-tolerance, time synchronization, and factors such as task affinities for communication. The protocol proposed in this paper gave an excellent packet delivery ratio, the lowest being 91% even with increased bandwidth usage when compared to IEEE 802.11.

Distributed cloud systems enable the distribution of computing resources across various geographical locations. While offering benefits like accelerated content delivery, the scalability and... Show More

Distributed cloud systems enable the distribution of computing resources across various geographical locations. While offering benefits like accelerated content delivery, the scalability and coherence maintenance of these systems pose significant challenges. Recent studies reveal shortcomings in existing distributed system schemes to meet modern cloud application demands and maintain coherence among heterogeneous system elements. This paper proposes a service-oriented network architecture for distributed cloud computing networks. Using a De Bruijn network as a software-defined overlay network, the architecture ensures scalability and coherence. Through service-based addressing, requests are issued to designated service address bands, streamlining service discovery. The architecture's evaluation through extensive simulations showcases sustainable scalability and inherent load-balancing properties. The paper concludes with insights into future research directions, emphasizing the extension of the proposed architecture to emerging distributed cloud use cases and decentralized security.

Various industrial applications deal with high-frequency data. Traditionally, these systems have analyzed high-frequency data directly on the data source or at the commanding PLC. However... Show More

Various industrial applications deal with high-frequency data. Traditionally, these systems have analyzed high-frequency data directly on the data source or at the commanding PLC. However, currently, Industry 4.0 technologies support new monitoring scenarios for high-frequency data monitoring where raw data is transmitted in soft-real time to an Edge/Fog or Cloud node for processing, enabling centralized computing. This demands efficient communication protocols capable of handling high-frequency, high-throughput data. This paper focuses on analyzing the performance of key IIoT (Industrial Internet of Things) messaging protocols—AMQP, MQTT, KAFKA, ZeroMQ, and OPCUA—to evaluate their suitability, in terms of latency and jitter, for transmitting high-frequency data within these new scenarios. The analysis reveals MQTT, AMQP, and ZeroMQ as top performers in Edge/Fog computing, while ZeroMQ exhibits the lowest latency and jitter in Cloud computing. Finally, a guideline for protocol selection is proposed, aiding industrial enterprises in protocol selection for specific AI use cases.