http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/104 2026-05-13T03:32:26Z 2026-05-13T03:32:26Z Osin, O.J Isah, Abdulkadir O. Subairu, Sikiru O. Ahmad, Suleiman Noel, Moses Dogonyaro http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30914 2026-05-06T19:05:53Z 2025-09-18T00:00:00Z

Title: Artifi cial Intelligence Enabled Ransomware: A Systematic Review Authors: Osin, O.J; Isah, Abdulkadir O.; Subairu, Sikiru O.; Ahmad, Suleiman; Noel, Moses Dogonyaro Abstract: Ransomware has been one of the most severe cyber threats, hobbled operations as well as bottom lines worldwide. Normally, ransomware attacks have been based on the idea of encrypting or blocking the data itself and paying a ransom for it to be released, yet the introduction of AI now also leaves many traditional detection methods out of date. This survey aims to answer three main research questions about how AI-empowered ransomware are growing as a threat: What are these emerging threats, state of-the-art detection methodologies to detect them and what are the related forthcoming research directions? Applying the PRISMA 2020 methodology, the retrieved papers between 2020 and 2025 were systematically reviewed, where only the ones of high quality and focus were included. What’s clear is that AI has turned ransomware from something that was statically built to something that’s now intelligent, work rounding, complex. However, there is hope on the horizon for detecting and defending against such attacks due to the advances in AI-based detection and response systems. In the following, we reflect on remaining challenges identified in the study and the importance of further work to bridge the gap and make AI-based defenses more impactful. Description: N/A

2025-09-18T00:00:00Z Joseph, Christiana C. Ojeniyi, Joseph Adebayo Noel, Moses Dogonyaro Ahmad, Suleiman Fashola, O.O Anyaora, Peter C. http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30913 2026-05-06T18:50:18Z 2025-09-16T00:00:00Z

Title: Artifi cial Intelligence – Powered Cyber Warfare: Resources, Methods, Weaponisation, Forensics, Threat Intelligence and Defenses: A Systematic Review Authors: Joseph, Christiana C.; Ojeniyi, Joseph Adebayo; Noel, Moses Dogonyaro; Ahmad, Suleiman; Fashola, O.O; Anyaora, Peter C. Abstract: Artifi cial Intelligence (AI) is gradually changing how cyber warfare works, creating new opportunities but also posing threats. This systematic literature review analyses how AI is used in diff erent areas, focusing on resources, methods, weaponisation, forensics, threat intelligence, defences and research directions. This study uses PRISMA-ScR methodology to analyse relevant publications published between 2018 and 2025. The fi ndings indicate that AI technologies are increasingly being utilised for reconnaissance, automated cyberatt acks, and enhanced threat detection. It also increases defensive measures by implementing intelligent systems that ensure faster response and mitigation. However, the increased reliance on AI raises serious concerns about ethical concerns, algorithmic bias, data privacy issues, and the high risk of autonomous weaponisation. The study highlights the need to improve AI-based defense systems and forensic methods to keep up with changing cyber threats. The paper ends by recommending more research in AI, stronger cybersecurity systems, and greater international cooperation to ensure AI is used responsibly in cyber warfare. Description: N/A

2025-09-16T00:00:00Z Hosea, I. G. Waziri, V. O. Ismaila, Idris Ojeniyi, Joseph Adebayo Olalere, Morufu Adebayo, O. S. http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30080 2025-07-31T17:25:32Z 2023-01-01T00:00:00Z

Title: A Machine Learning Approach to Fake News Detection Using Support Vector Machine (SVM) and Unsupervised Learning Model Authors: Hosea, I. G.; Waziri, V. O.; Ismaila, Idris; Ojeniyi, Joseph Adebayo; Olalere, Morufu; Adebayo, O. S. Abstract: Blogging over the years have become a lucrative business, the bloggers main aim is to attract people to his or her blog. In the quest for that, many blogs or page post fake news by using enticing captions to captivate the minds of readers. The captions are mostly displayed on social media and by clicking on the captions, the reader will be redirected to the blog where the news is been posted. The posted fake news can sometimes lead to misinformation to the public, violence, inciting conflict and extreme cases, death. Many works have been done on fake news detection with good accuracy rate in terms of detecting fake news. This paper presents an effective way of detecting fake news using Support Vector Machine (SVM) and Lagrangian Duality which yielded an accuracy of 95.74%.

2023-01-01T00:00:00Z Muhammad, M. K. Olaniyi, O. M. Osang, F. Oyefolahan, I. O. Ojeniyi, Joseph Adebayo Kolo, I. M. http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/30079 2025-07-31T17:08:20Z 2024-01-01T00:00:00Z

Title: Learners’ Privacy-Preserving Scheme for Ranking Data Sensitivity in Mobile Learning System Authors: Muhammad, M. K.; Olaniyi, O. M.; Osang, F.; Oyefolahan, I. O.; Ojeniyi, Joseph Adebayo; Kolo, I. M. Abstract: Mobile Learning System (MLS) is facing new challenges in terms of privacy, such as data collection, storage, and sharing because of the core infrastructure and network that enables cloud computing services. Most of the data exchange in Mobile Learning System (MLS) require mandatory authorisation to allow access to the learners’ information in the MLS. Therefore, this article attempts to rank learners' sensitive attributes stored in MLS. Thus, concerns about privacy breaches motivated this paper to adopt an attributes partitioning strategy into the sensitive attributes to enforce privacy during learners’ profile information access. The article adopted the informed consent phenomenon to determine and formulate learners’ data privacy attributes sensitivity using the Fuzzy Analytic Hierarchy Process (FAHP) Algorithm. Results from the implemented Learners’ Privacy Preserving (LPP) Algorithm determined normalized weights of top-five rank-selected learners’ sensitive data to include: Browsing History (1ST, Ranked), Geolocation Data (2ND, Ranked), IP Address (3RD, Ranked), web Browser (4TH, Ranked), Medical Records (5TH, Ranked) and CGPA (10TH, Ranked) respectively. This indicates that these five most sensitive features are at risk and require protection to prevent privacy breaches, thus ensuring privacy preservation that prevents unauthorised access to learners’ sensitive data in the mobile learning system environment. The ranking of sensitive data in this paper could serve as inspiration for future research work on mobile learning security to improve the privacy of sensitive attributes in MLS environment

2024-01-01T00:00:00Z