Browsing by Author "Wamusi, Robert"

Now showing 1 - 6 of 6
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    A Comprehensive review on cryptographic techniques for securing internet of medical things: A state-of-the-art, applications, security attacks, mitigation measures, and future research direction.
    (Mesopotamian Journal of Artificial Intelligence in Healthcare, 2024-11-30) Wamusi, Robert; Asiku, Denis; Adebo, Thomas; Aziku, Samuel; Kabiito, Simon Peter; Zaward, Morish; Guma, Ali
    As healthcare becomes increasingly dependent on the Internet of Medical Things (IoMT) infrastructure, it is essential to establish a secure system that guarantees the confidentiality and privacy of patient data. This system must also facilitate the secure sharing of healthcare information with other parties within the healthcare ecosystem. However, this increased connectivity also introduces cybersecurity attacks and vulnerabilities. This comprehensive review explores the state-of-the-art in the IoMT, security requirements in the IoMT, cryptographic techniques in the IoMT, application of cryptographic techniques in securing the IoMT, security attacks on cryptographic techniques, mitigation strategies, and future research directions. The study adopts a comprehensive review approach, synthesizing findings from peer-reviewed journals, conference proceedings, book chapters, Books, and websites published between 2020 and 2024 to assess their relevance to cryptographic applications in IoMT systems. Despite advancements, cryptographic algorithms in IoMT remain susceptible to security attacks, such as man-in-the-middle attacks, replay attacks, ransomware attacks, cryptanalysis attacks, key management attacks, chosen plaintext/chosen ciphertext attacks, and side-channel attacks. While techniques like homomorphic encryption enhance security, their high computational and power demands pose challenges for resource-constrained IoMT devices. The rise of quantum computing threatens the efficacy of current cryptographic protocols, highlighting the need for research into quantum-resistant cryptography. The review identifies critical gaps in existing cryptographic research and emphasizes future directions, including lightweight cryptography, quantum-resistant methods, and artificial intelligence-driven security mechanisms. These innovations are vital for meeting the growing security requirements of IoMT systems and protecting against increasingly sophisticated threats.
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    A Survey on artificial intelligence and blockchain applications in cybersecurity for smart cities
    (2025-01-10) Asiku, Denis; Adebo, Thomas; Wamusi, Robert; Aziku, Samuel; Kabiito, Simon Peter; Zaward, Morish; Sallam, Malik; Ali, Guma; Mijwil, Maad M.
    Smart cities rapidly evolve into transformative ecosystems where advanced technologies work together to improve urban living. These interconnected environments use emerging technologies to offer efficient services and sustainable solutions for urban challenges. As these systems become more complex, their vulnerability to cybersecurity threats also increases. Integrating artificial intelligence (AI) and Blockchain technologies to address these challenges presents promising solutions that ensure secure and resilient infrastructures. This study provides a comprehensive survey of integrating AI, Blockchain, cybersecurity, and smart city technologies based on an analysis of peer-reviewed journals, conference proceedings, book chapters, and websites. Seven independent researchers reviewed relevant literature published between January 2021 and December 2024 using ACM Digital Library, Wiley Online Library, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, IGI Global, and Google Scholar. The study explores how AI can enhance threat detection, anomaly detection, and predictive analytics, enabling real-time responses to cyber threats. It examines various AI methodologies, including machine learning and deep learning, to identify vulnerabilities and prevent attacks. It discusses the role of Blockchain in securing data integrity, improving transparency, and providing decentralized control over sensitive information. Blockchain’s tamper-proof ledger and smart contract capabilities offer innovative solutions for identity management, secure transactions, and data sharing among smart city stakeholders. The study also highlights how combining AI and Blockchain can create robust cybersecurity frameworks, enhancing resilience against emerging threats. The survey concludes by outlining future research directions and offering recommendations for policymakers, urban planners, and cybersecurity professionals. This study identifies emerging trends and applications for enhancing the security and resilience of smart cities through innovative technological solutions. The survey provides valuable insights for researchers and practitioners who aim to utilize AI and Blockchain to improve smart city cybersecurity.
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    Deep learning-based neural network modeling for economic performance prediction: An empirical study on Iraq
    (Peninsula Publishing Press, 2025-02-20) Shaker, Atheel Sabih; Ali, Guma; Wamusi, Robert; Habib, Hassan
    This study investigates the application of deep learning-based neural networks for predicting Iraq’s economic performance. Traditional econometric models impose restrictive assumptions that limit their predictive accuracy, especially in volatile economic environments. To overcome these limitations, we propose an artificial neural network (ANN) model trained on six key macroeconomic indicators: Gross Domestic Product (GDP), inflation rate, unemployment rate, exchange rate, trade volume, and government spending. The dataset spans from 2000 to 2023, sourced from authoritative economic institutions. The methodology incorporates feature scaling, hyperparameter tuning, and backpropagation optimization to minimize mean squared error (MSE) and enhance generalization performance. The model is validated through cross-validation and out-of-sample testing. Descriptive statistical analysis highlights the variability of macroeconomic indicators, while the ANN model effectively captures nonlinear dependencies. The results indicate that GDP and government spending are the most influential factors in economic performance prediction, while unemployment rate and exchange rate exhibit lower predictive significance. The model demonstrates superior accuracy compared to traditional regression-based approaches, with minimal error in both training and testing phases. This research contributes to the empirical literature on machine learning in economic forecasting by presenting a robust alternative to conventional predictive models. The findings provide policymakers with valuable insights for designing data-driven economic policies. Future work should explore hybrid models integrating deep learning with traditional econometrics to improve interpretability while maintaining predictive power.
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    Integration of artificial intelligence, blockchain, and quantum cryptography for securing the Industrial Internet of Things (IIoT): Recent advancements and future trends
    (Mesopotamian Academic Press, 2025-03-27) Ali, Guma; Aziku, Samue; Kabiito, Simon Peter; Morish, Zaward; Adebo, Thomas; Wamusi, Robert; Asiku, Denis; Sallam, Malik; Mijwil, Maad M.; Ayad, Jenan; Salau, Ayodeji Olalekan; Dhoska, Klodian
    The swift growth of the Industrial Internet of Things (IIoT) offers tremendous potential to boost productivity, facilitate real-time decision-making, and automate procedures in various industries. However, as industries increasingly adopt IIoT, they face paramount data security, privacy, and system integrity challenges. Artificial intelligence (AI), Blockchain, and quantum cryptography are gaining significant attention as solutions to address these challenges. This paper comprehensively surveys advanced technologies and their potential applications for securing IIoT ecosystems. It reviews findings from 196 sources, including peer-reviewed journal articles, conference papers, books, book chapters, reports, and websites published between 2021 and 2025. The survey draws insights from leading platforms like Springer Nature, ACM Digital Library, Frontiers, Wiley Online Library, Taylor & Francis, IGI Global, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar. This paper explores AI-driven approaches to anomaly detection, predictive maintenance, and adaptive security mechanisms, demonstrating how machine learning (ML) and deep learning (DL) can identify and mitigate threats instantly. It also examines Blockchain technology, emphasizing its decentralized nature, immutability, and ability to secure data sharing and authentication within IIoT networks. The paper discusses quantum cryptography, which utilizes quantum mechanics for theoretically unbreakable encryption, ensuring secure communications in highly sensitive industrial environments. The integration of these technologies is analyzed to create a multi-layered defense against cyber threats, highlighting challenges in scalability, interoperability, and computational overhead. Finally, the paper reviews the current research, limitations and challenges, and future directions for securing IIoT with these advanced technologies. This survey offers valuable insights to researchers, engineers, and industry practitioners working to secure the expanding IIoT infrastructure.
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    Practical application and management of information communication technology (ict) to enhance the performance of Ugandan secondary schools in West Nile
    (East African Nature and Science Organization, 2025-01-05) Wamusi, Robert; Habibu, Taban
    This study focuses on the contribution that Information and Communication Technology (ICT) can make to improving teacher and student performance in secondary schools in West Nile Region, Uganda. Examines the existing ICT implementation, assesses integration and potential benefits issues, and analyses the relationship between ICT usage and learning outcomes. The research is focused on showing that ICT can go global and how it connects Uganda’s secondary schools in West Nile to global education networks. ICT enhances global learning through active participation, innovation, and flexibility through access to international resources and embracing cross-boundary collaboration and virtual interchanges. This has improved student achievement but also endows students and teachers with the competencies to prosper in a globally connected environment. Ugandan schools have poorly developed ICT facilities, but schools embrace ICT education and facilities for operations. The study centers on specific ICT issues in West Nile schools and explores the possibility of using ICT to raise aggregate performance and efficiency in communication, collaboration, and organizational management. Quantitative and qualitative data were collected from 400 respondents, including teachers and students from 10 secondary schools. Data collection tools included questionnaires and interviews. Quantitative analysis was performed using SPSS, while NVivo was used for qualitative analysis. Ethical considerations were strictly adhered to protect participants' rights. While observing ICT integration in teaching across the ten selected secondary schools in West Nile and surveying 100 teachers, researchers found that 55.6% of them sometimes, 33.3% consistently, and 11% seldom integrate ICT in their teaching. This limited integration is due to a lack of ICT equipment, for example, computers, projectors, and internet connections; inadequate teacher education, where the majority of the teachers are found to be either lacking skills or self-confidence to incorporate ICT in teaching; and limited resource availability where even schools that have procured ICT tools are most often found to be having very few that are inadequacy for the needs of both teacher and students to make effective use of. These results raise concerns regarding the existing disparities in developments and funds for ICT training in West Nile’s secondary schools, with recommendations being made to enhance specific plans to reduce the digital divide. These include making ICT tools a focal area, increasing internet connection, and providing training activities that would increase and develop the competencies of the teaching staff. They seek to devise a technological learning atmosphere to enhance education, teacher, and student learning outcomes.
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    Securing the internet of wetland things (IoWT) using machine and deep learning methods: a survey
    (Mesopotamian journal of Computer Science, 2025-02-03) Ali, Guma; Wamusi, Robert; Mijwil, Maad M.; Sallam, Malik; Ayad, Jenan; Adamopoulos, Ioannis
    Wetlands are essential ecosystems that provide ecological, hydrological, and economic benefits. However, human activities and climate change are degrading their health and jeopardizing their long-term sustainability. To address these challenges, the Internet of Wetland Things (IoWT) has emerged as an innovative framework integrating advanced sensing, data collection, and communication technologies to monitor and manage wetland ecosystems. Despite its potential, the IoWT faces substantial security and privacy risks, compromising its effectiveness and hindering adoption. This survey explores integrating machine learning (ML) and deep learning (DL) techniques as solutions to address the security threats, vulnerabilities, and challenges inherent in IoWT ecosystems. The survey examines findings from 231 sources, encompassing peer-reviewed journal articles, conference papers, books, book chapters, and websites published between 2020 and 2025. It consolidates insights from prominent platforms such as the Springer Nature, Emerald Insight, ACM Digital Library, Frontiers, Wiley Online Library, SAGE, Taylor & Francis, IGI Global, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar. Machine learning and DL methods have proven highly effective in detecting adversarial attacks, identifying anomalies, recognizing intrusions, and uncovering man-in-the-middle attacks, which are crucial in securing systems. These techniques also focus on detecting phishing, malware, and DoS/DDoS attacks and identifying insider and advanced persistent threats. They help detect botnet attacks and counteract jamming and spoofing efforts, ensuring comprehensive protection against a wide range of cyber threats. The survey examines case studies and the unique requirements and constraints of IoWT systems, such as limited energy resources, diverse sensor networks, and the need for real-time data processing. It also proposes future directions, such as developing lightweight, energy-efficient algorithms that operate effectively within the constrained environments typical of IoWT applications. Integrating ML and DL methods strengthens IoWT security while protecting and preserving wetlands through intelligent and resilient systems. These findings offer researchers and practitioners valuable insights into the current state of IoWT security, helping them drive and shape future advancements in the field.