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Mohamed Ibrahim Maraicar, Haroon Ali (2024) Enhancing Network Security by Detecting Rogue Access Points using Ensemble Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.
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Abstract
Unauthorised access points (APs) in wireless networks pose substantial threat to security, potentially resulting in data breaches and unauthorised access. Conventional security measures frequently fail to successfully identify these breaches, requiring the use of more sophisticated methods. considering the increasing reliance on wireless networks, especially in contexts where security is crucial, it is essential to create strong detection systems to identify unauthorised access points and protect the integrity of the network. This study utilised ensemble machine learning models, specifically Random Forest, Gradient Boosting, and AdaBoost, along with ANOVA feature selection, to identify rogue Access Points (APs) using the AWID dataset. The application of ensemble approaches, enhanced through the utilisation of Grid Search and cross-validation, greatly enhanced the accuracy of detection. The results indicated that the ensemble models surpassed the traditional models, with Gradient Boosting obtaining the highest level of accuracy. The SMOTE method was utilised to tackle the issue of data imbalance, resulting in improved model performance. However, the evaluation of certain metrics encountered difficulties due to constraints in computational resources. The results of this study add to the existing body of knowledge on network security by showing that ensemble learning approaches are more effective than traditional techniques such as KNN and SVM in identifying rogue access points (APs). The created model provides a reliable tool for network administrators, potentially reducing the likelihood of data breaches. Future research should prioritise the integration of threat intelligence to improve detection capabilities and investigate the system's capacity to recognise certain types of attacks.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Khan, Imran UNSPECIFIED |
| Uncontrolled Keywords: | AWID dataset; Machine learning classifiers; ANOVA; Access Points |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Cyber Security |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 30 Jul 2025 11:26 |
| Last Modified: | 30 Jul 2025 11:26 |
| URI: | https://norma.ncirl.ie/id/eprint/8340 |
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