Published: Nov 28, 2023
Converted to Gold OA:
DOI: 10.4018/IJCAC.334214
Volume 14
Muath AlShaikh, Waleed Alsemaih, Sultan Alamri, Qusai Ramadan
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...
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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.
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AlShaikh, Muath, et al. "Using Supervised Learning to Detect Command and Control Attacks in IoT." IJCAC vol.14, no.1 2024: pp.1-19. http://doi.org/10.4018/IJCAC.334214
APA
AlShaikh, M., Alsemaih, W., Alamri, S., & Ramadan, Q. (2024). Using Supervised Learning to Detect Command and Control Attacks in IoT. International Journal of Cloud Applications and Computing (IJCAC), 14(1), 1-19. http://doi.org/10.4018/IJCAC.334214
Chicago
AlShaikh, Muath, et al. "Using Supervised Learning to Detect Command and Control Attacks in IoT," International Journal of Cloud Applications and Computing (IJCAC) 14, no.1: 1-19. http://doi.org/10.4018/IJCAC.334214
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Published: Dec 1, 2023
Converted to Gold OA:
DOI: 10.4018/IJCAC.334364
Volume 14
Utsav Upadhyay, Alok Kumar, Gajanand Sharma, Ashok Kumar Saini, Varsha Arya, Akshat Gaurav, Kwok Tai Chui
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...
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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.
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Upadhyay, Utsav, et al. "Mitigating Risks in the Cloud-Based Metaverse Access Control Strategies and Techniques." IJCAC vol.14, no.1 2024: pp.1-30. http://doi.org/10.4018/IJCAC.334364
APA
Upadhyay, U., Kumar, A., Sharma, G., Saini, A. K., Arya, V., Gaurav, A., & Chui, K. T. (2024). Mitigating Risks in the Cloud-Based Metaverse Access Control Strategies and Techniques. International Journal of Cloud Applications and Computing (IJCAC), 14(1), 1-30. http://doi.org/10.4018/IJCAC.334364
Chicago
Upadhyay, Utsav, et al. "Mitigating Risks in the Cloud-Based Metaverse Access Control Strategies and Techniques," International Journal of Cloud Applications and Computing (IJCAC) 14, no.1: 1-30. http://doi.org/10.4018/IJCAC.334364
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Published: Feb 20, 2024
Converted to Gold OA:
DOI: 10.4018/IJCAC.339200
Volume 14
Hadeel Al-Obaidy, Aysha Ebrahim, Ali Aljufairi, Ahmed Mero, Omar Eid
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...
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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.
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Al-Obaidy, Hadeel, et al. "Software Engineering for Developing a Cloud Computing Museum-Guide System." IJCAC vol.14, no.1 2024: pp.1-19. http://doi.org/10.4018/IJCAC.339200
APA
Al-Obaidy, H., Ebrahim, A., Aljufairi, A., Mero, A., & Eid, O. (2024). Software Engineering for Developing a Cloud Computing Museum-Guide System. International Journal of Cloud Applications and Computing (IJCAC), 14(1), 1-19. http://doi.org/10.4018/IJCAC.339200
Chicago
Al-Obaidy, Hadeel, et al. "Software Engineering for Developing a Cloud Computing Museum-Guide System," International Journal of Cloud Applications and Computing (IJCAC) 14, no.1: 1-19. http://doi.org/10.4018/IJCAC.339200
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Published: Feb 26, 2024
Converted to Gold OA:
DOI: 10.4018/IJCAC.339563
Volume 14
Ahmad Althunibat, Bayan Alsawareah, Siti Sarah Maidin, Belal Hawashin, Iqbal Jebril, Belal Zaqaibeh, Haneen A. Al-khawaja
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...
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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.
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Althunibat, Ahmad, et al. "Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms." IJCAC vol.14, no.1 2024: pp.1-19. http://doi.org/10.4018/IJCAC.339563
APA
Althunibat, A., Alsawareah, B., Maidin, S. S., Hawashin, B., Jebril, I., Zaqaibeh, B., & Al-khawaja, H. A. (2024). Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms. International Journal of Cloud Applications and Computing (IJCAC), 14(1), 1-19. http://doi.org/10.4018/IJCAC.339563
Chicago
Althunibat, Ahmad, et al. "Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms," International Journal of Cloud Applications and Computing (IJCAC) 14, no.1: 1-19. http://doi.org/10.4018/IJCAC.339563
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Published: Feb 27, 2024
Converted to Gold OA:
DOI: 10.4018/IJCAC.339891
Volume 14
Manoj Subhash Kakade, K.R. Anupama, Sushil Nayak, Swarnab Garang
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...
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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.
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Kakade, Manoj Subhash, et al. "Custom Network Protocol Stack for Communication Between Nodes in a Cloudlet System." IJCAC vol.14, no.1 2024: pp.1-24. http://doi.org/10.4018/IJCAC.339891
APA
Kakade, M. S., Anupama, K., Nayak, S., & Garang, S. (2024). Custom Network Protocol Stack for Communication Between Nodes in a Cloudlet System. International Journal of Cloud Applications and Computing (IJCAC), 14(1), 1-24. http://doi.org/10.4018/IJCAC.339891
Chicago
Kakade, Manoj Subhash, et al. "Custom Network Protocol Stack for Communication Between Nodes in a Cloudlet System," International Journal of Cloud Applications and Computing (IJCAC) 14, no.1: 1-24. http://doi.org/10.4018/IJCAC.339891
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Published: Feb 27, 2024
Converted to Gold OA:
DOI: 10.4018/IJCAC.339892
Volume 14
Osama R. S. Ramadan, Mohamed Yasin I. Afifi, Ahmed Yahya
Distributed cloud systems enable the distribution of computing resources across various geographical locations. While offering benefits like accelerated content delivery, the scalability and...
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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.
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Ramadan, Osama R. S., et al. "A Distributed Cloud Architecture Based on General De Bruijn Overlay Network." IJCAC vol.14, no.1 2024: pp.1-19. http://doi.org/10.4018/IJCAC.339892
APA
Ramadan, O. R., Afifi, M. Y., & Yahya, A. (2024). A Distributed Cloud Architecture Based on General De Bruijn Overlay Network. International Journal of Cloud Applications and Computing (IJCAC), 14(1), 1-19. http://doi.org/10.4018/IJCAC.339892
Chicago
Ramadan, Osama R. S., Mohamed Yasin I. Afifi, and Ahmed Yahya. "A Distributed Cloud Architecture Based on General De Bruijn Overlay Network," International Journal of Cloud Applications and Computing (IJCAC) 14, no.1: 1-19. http://doi.org/10.4018/IJCAC.339892
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Published: Apr 26, 2024
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DOI: 10.4018/IJCAC.342128
Volume 14
Telmo Fernández De Barrena Sarasola, Ander García, Juan Luis Ferrando
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...
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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.
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Sarasola, Telmo Fernández De Barrena, et al. "IIoT Protocols for Edge/Fog and Cloud Computing in Industrial AI: A High Frequency Perspective." IJCAC vol.14, no.1 2024: pp.1-30. http://doi.org/10.4018/IJCAC.342128
APA
Sarasola, T. F., García, A., & Ferrando, J. L. (2024). IIoT Protocols for Edge/Fog and Cloud Computing in Industrial AI: A High Frequency Perspective. International Journal of Cloud Applications and Computing (IJCAC), 14(1), 1-30. http://doi.org/10.4018/IJCAC.342128
Chicago
Sarasola, Telmo Fernández De Barrena, Ander García, and Juan Luis Ferrando. "IIoT Protocols for Edge/Fog and Cloud Computing in Industrial AI: A High Frequency Perspective," International Journal of Cloud Applications and Computing (IJCAC) 14, no.1: 1-30. http://doi.org/10.4018/IJCAC.342128
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Published: Aug 28, 2024
Converted to Gold OA:
DOI: 10.4018/IJCAC.353301
Volume 14
Abdelraouf Ishtaiwi, Ali Mohd Ali, Ahmad Al-Qerem, Mohammad Sabahean, Bilal Alzubi, Ammar Almomani, Mohammad Alauthman, Amjad Aldweesh, Mohammad A. Al Khaldy
Machine learning has become ubiquitous across industries for its ability to uncover in- sights from data. This research explores the application of machine learning for identifying phishing...
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Machine learning has become ubiquitous across industries for its ability to uncover in- sights from data. This research explores the application of machine learning for identifying phishing websites. The efficiency of different algorithms at classifying malicious sites is evaluated and contrasted. By exposing the risks of phishing, the study aims to develop reliable systems for fake website detection. The results showcase machine learning's capabilities for augmented cybersecurity through automated threat intelligence. Phishing employs social engineering techniques to disguise malicious links as trusted entities, tricking victims into revealing sensitive information. This work investigates phishing detection leveraging curated lists and machine learning for adaptive defense.
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Ishtaiwi, Abdelraouf, et al. "Next-Gen Phishing Defense Enhancing Detection With Machine Learning and Expert Whitelisting/Blacklisting." IJCAC vol.14, no.1 2024: pp.1-17. http://doi.org/10.4018/IJCAC.353301
APA
Ishtaiwi, A., Ali, A. M., Al-Qerem, A., Sabahean, M., Alzubi, B., Almomani, A., Alauthman, M., Aldweesh, A., & Al Khaldy, M. A. (2024). Next-Gen Phishing Defense Enhancing Detection With Machine Learning and Expert Whitelisting/Blacklisting. International Journal of Cloud Applications and Computing (IJCAC), 14(1), 1-17. http://doi.org/10.4018/IJCAC.353301
Chicago
Ishtaiwi, Abdelraouf, et al. "Next-Gen Phishing Defense Enhancing Detection With Machine Learning and Expert Whitelisting/Blacklisting," International Journal of Cloud Applications and Computing (IJCAC) 14, no.1: 1-17. http://doi.org/10.4018/IJCAC.353301
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