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    DEVELOPMENT OF A HIERARCHICAL ANOMALY DETECTION MODEL IN A FEDERATED CLOUD INFRASTRUCTURE USING ENHANCED GRAPH SAMPLING AND AGGREGATION
    (Covenant University Ota, 2025-08) LAWAL, Comfort Oluwaseyi; Covenant University Dissertation
    Modern distributed computing systems generate massive volumes of log data, making manual analysis infeasible. Existing methods treat log entries as independent events, failing to leverage structural dependencies and temporal correlations. This limitation is critical in federated cloud infrastructures where anomalies propagate across interconnected services. This research developed a hierarchical anomaly detection model that employs Federated Hierarchical Graph Sampling and Aggregation (Fed-HiGraphSAGE) techniques to enable multi-level anomaly classification in distributed cloud environments while preserving data privacy. FedHiGraphSAGE was built on an Enhanced Hierarchical GraphSAGE architecture, incorporating node features, edge attributes, and hierarchical structure to classify anomalies across five semantic levels: Anomaly, Anomaly-Type, Cloud Component, Application-Type and Specific-cloud-module. The model employs federated learning capabilities, dynamic graph management, hierarchical diagnostic capabilities, adaptive thresholding, and memory-efficient training. It also implemented a HierarchicalStratifiedBalancer to address class imbalance. The model was trained and evaluated using federated learning across three data-contributing regions: Afe Babalola University, Landmark University, and DRC_Congo, with Covenant University serving as the federated learning coordinator. A total of 54,919 system logs were processed from these three regions to simulate real-world federated deployment. The model demonstrated exceptional performance with region-specific accuracies of 91.97% (Afe Babalola), 98.27% (Landmark), and 98.76% (DRC_Congo). Hierarchical metrics confirmed effective multi-level classification with h-precision ranging from 91.82% to 98.99%, h-recall from 90.60% to 98.53%, and h-f1 from 89.95% to 98.66%. The model generated detailed hierarchical anomaly classifications and demonstrated significant performance adaptability across regions while maintaining global model coherence, with federated training reducing the global client’s loss from approximately 0.47 to 0.02 over 15 rounds. This research advances automated system monitoring by demonstrating that federated learning with graph-based representations and hierarchical classification significantly improves anomaly detection performance while enabling cross-regional knowledge sharing. The model’s ability to maintain exceptional performance across multiple classification levels while providing explainable results establishes a new benchmark for automated log analysis in complex distributed systems
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    DEVELOPMENT OF A PAIRING-ANOMALY DETECTION SYSTEM IN VIDEO SURVEILLANCE USING OBJECT DETECTION MODEL
    (2025-03) SODIPO, QUEEN BUSAYO; Covenant University Diseertation
    Anomaly detection in social behaviours among students is critical to maintaining a safe and respectful academic environment. This research focuses on developing an object detection-based pairing-anomaly detection system to identify and classify unusual social behaviours, such as hand-holding, which can be perceived as an anomaly and potentially lead to more serious issues like sexual misconduct. Object detection, a most significant branch of computer vision, attempts to locate and identify objects in images or video frames. The other is anomaly detection, identifying data sets that do not follow expected behaviour configurations. Combining object detection and anomaly detection approaches provides a powerful solution to detect and flag anomalous or problematic behaviour in any setting. Object detection-based models have recently gained significant attention for their efficiency in detecting and identifying anomalies of interest in complex scenes at high precision. This study shows the application of deep learning for anomalous event detection with an object detection architecture. Utilising the YOLO (You Only Look Once) architecture, the system is designed to detect and localise anomalies in real-time. The model is trained on a custom holding hands dataset, specifically designed to capture instances of hand-holding alongside the Pascal VOC (Visual Object Classes) benchmark dataset, to ensure versatility across varied scenarios. This approach incorporates transfer learning and data augmentation techniques to and optimise model performance with limited labelled data. Evaluation metrics, including mean accuracy precision (mAP), recall, F1 score, and AUC (Area Under Curve), demonstrate the model's effectiveness, with the Custom Holding Hands Dataset achieving an impressive mAP score of 99.5%. The system is integrated into a web application, enabling real-time anomaly detection and classification. This research contributes to developing computer vision-based pairing-anomaly detection systems for social behaviour analysis, with potential applications in maintaining a safe and respectful academic environment.