http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/105 2026-05-03T18:12:03Z 2026-05-03T18:12:03Z Adamu, Abubakar Opeyemi, Aderiike Abisoye Alabi, Isiaq Oludare Adepoju, Solomon Oyefolahan, Ishaq Oyebisi http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/29907 2025-05-30T08:20:45Z 2025-02-27T00:00:00Z

Title: REVIEW OF DATA-DRIVEN AND MODEL-BASED PIPELINE MONITORING AND LEAKAGE DETECTION TECHNIQUES Authors: Adamu, Abubakar; Opeyemi, Aderiike Abisoye; Alabi, Isiaq Oludare; Adepoju, Solomon; Oyefolahan, Ishaq Oyebisi Abstract: Pipeline leakage detection and monitoring systems are crucial for ensuring the safety, efficiency, and reliability of pipeline infrastructure, which is vital for economic growth, environmental protection and public safety. This review provides a comprehensive overview of data-driven and model-based approaches for pipeline leakage detection and monitoring. Existing literatures on advanced data analytics techniques, including machine learning, statistical process control, and model-based methods, such as pressure transient analysis and inverse transient analysis are examine. Furthermore, the review highlights the strengths and limitations of each approach, discusses the challenges associated with pipeline leakage detection, and identifies future research directions and conclude by providing insights that can be adopted for the development of more effective and efficient pipeline leakage detection and monitoring systems, ultimately contributing to the reduction of pipeline failures and environmental impacts.

2025-02-27T00:00:00Z Shuaibu, Y. B. Alabi, I. O. http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/29906 2025-05-30T08:20:07Z 2024-07-19T00:00:00Z

Title: Utilizing Metaheuristic Ensemble Feature Selection to Enhance Intrusion Detection Systems Authors: Shuaibu, Y. B.; Alabi, I. O. Abstract: Intrusion detection plays a crucial role in ensuring the security of computer networks by identifying and preventing unauthorized access or malicious activities. This thesis aim to develop an advanced intrusion detection model by integrating Gravitational search Algorithm (GSA) with Grey wolf Optimization (GWO) algorithm to optimize its performance. The proposed model will combine the strength of GSA-GWO in classification with the optimization capabilities of GWO to enhance the accuracy and efficiency of intrusion detection systems. The research methodology will begin with the selection of appropriate datasets, representative of real-world network traffic, for training and testing the intrusion detection model. Preprocessing techniques will be applied to prepare the datasets, including feature selection and normalization, to ensure the model's robustness and effectiveness. The GSA algorithm will then be implemented and configured, with suitable kernel functions and hyperparameter tuning, to train the intrusion detection model. To optimize the performance of the GSGW-DT model, the Grey Wolf Optimization algorithm will be employed. GWO will mimic the bio behavior of Gey wolves to search for optimal solutions in complex problem spaces. By incorporating GWO, the intrusion detection model will be able to fine-tune BGSA-BGWO parameters and select relevant features, thereby improving accuracy, reducing false positives/negatives, and enhancing overall performance. The developed model will be evaluated using various performance metrics, including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Comparative analysis will be conducted to assess the superiority of the GSA-GWO model over baseline models or existing intrusion detection approaches. Furthermore, an in-depth analysis of the results will highlight the strengths, weaknesses, and optimization benefits achieved by the proposed model. The conclusion will summarize the key findings and contributions of the study, emphasizing the effectiveness of the GSA-GWO model in optimizing intrusion detection performance. Limitations and potential areas for improvement will be discussed, paving the way for future research. Future work will include exploring additional optimization algorithms, evaluating realtime intrusion detection scenarios, and investigating hybrid approaches to further enhance the model's accuracy and robustness.

2024-07-19T00:00:00Z Sule, Aishat A. Alhassan, John K. Ismaila, I. Alabi, I. O. Subairu, S. O. http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/29905 2025-05-30T08:18:57Z 2024-08-15T00:00:00Z

Title: A NOVEL DEEP BELIEF-BASED MODEL FOR THE INTRUSION DETECTION OF NETWORK TRAFFIC Authors: Sule, Aishat A.; Alhassan, John K.; Ismaila, I.; Alabi, I. O.; Subairu, S. O. Abstract: The fast expansion of networked systems,combined with the pervasive reliance on the internet, has raised worries about network security, needing new defense methods.Intrusion Detection Systems (IDS) use a variety of ways to distinguish between legitimateand malicious network traffic, including rule-based,signature-based, anomaly detection, and machine learning approaches. While signature-based IDS excel at detecting known threats,they struggle with novel attacks, prompting the development of anomaly-based IDS and machine learning methods such as Random Forest and Logistic Regression, among others.However, these techniques have scalability and computational complexity challenges.Cybersecurity remains a significant concern for organizations due to continual cyber-attack threats, which drives ongoing development into intrusion detection systems. Deep Learning (DL)-based IDSs have gained popularity due to their deep feature learning capabilities, while being resource-intensive. To overcome computational problems, this research provides an optimized deep belief-based model that combines the Genetic Algorithm, Particle Swarm Optimization, and Probabilistic Neural Network (GePP-Dbnet).This model seeks to find a balance between accuracy, training duration, and false alarm rates while identifying a diverse set of threat classes. Validation will be carried out using the benchmark datasets NSL-KDD and CSE-CIC-IDS2018, which provide realistic scenarios for assessing the model's effectiveness.

2024-08-15T00:00:00Z Hussaini, Shamsudeen Alabi, Isiaq Oludare Ojerinde, Oluwaseun Adeniyi http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/29904 2025-05-30T08:17:37Z 2024-07-01T00:00:00Z

Title: Anti-Spoofing Detection Model Using Transfer Learning Techniques for Smart Door Security Systems Authors: Hussaini, Shamsudeen; Alabi, Isiaq Oludare; Ojerinde, Oluwaseun Adeniyi Abstract: This study introduces a robust anti-spoofing detection model specifically designed for smart door security systems, targeting critical vulnerabilities present in current facial recognition technologies. Utilising transfer learning-based architectures, particularly VGG16 and MobileNet, the proposed approach integrates pre-trained weights alongside advanced image augmentation techniques to improve the model's capability to identify various spoofing attacks, including print, replay, and 3D mask attacks. The VGG16-based model achieved an impressive accuracy of 98.75%, while the MobileNet-based model recorded an accuracy of 97.82%, showcasing exceptional performance in differentiating between genuine and spoofed images. Evaluations using metrics such as precision, recall, and F1- score further confirmed the robustness and efficiency of the models. With its real-time applicability and computational efficiency, this system is well-suited for deployment in smart homes and IoT-enabled security frameworks. By addressing limitations related to dataset generalisation, robustness, and scalability, this research significantly enhances the reliability and security of biometric-based authentication systems, offering a scalable framework for future smart security applications.

2024-07-01T00:00:00Z