An optimized fast learning network model for real time intrusion detection in network security
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Date
2025-10-22
Authors
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Journal ISSN
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Publisher
Muni University
Abstract
Traditional Intrusion Detection Systems (IDSs) often struggle with high false alarm rates, lengthy training periods, and challenges in quickly identifying emerging threats, such as DDoS and phishing attacks. At Uganda Christian University (UCU), where IDS relies on network traffic analysis, these issues can hinder early threat detection and prompt a rapid response. This research examines the current network threat landscape at UCU, focusing on its types, characteristics, and attack strategies. To address data imbalance and high dimensionality, various techniques, including configurations of the Synthetic Minority Over-sampling Technique (SMOTE), Random Forest, XGBoost, AdaBoost, Decision Trees, and Convolutional Neural Networks (CNNs), are employed. The best results were obtained with Random Forest combined with SMOTE, achieving an accuracy of 81.88%, a precision of 82.17%, a recall of 81.88%, and an F1-score of 80.19%. This model approach performed better than traditional algorithms in terms of accuracy and speed, which can be implemented in university networks.
Description
A dissertation submitted to the faculty of technoscience in partial fulfillment of the requirements for the award of the degree of master of science in computer science of Muni University
Keywords
Learning network model, Intrusion detection, Network security
Citation
Wamusi, R. (2025). An optimized fast learning network model for real time intrusion detection in network security (Unpublished graduate dissertation). Muni University, Arua, Uganda