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Shaju, Albin (2024) Exploring Machine Learning Approaches for Robust Anomaly Detection and Responsive Security in IoT Frameworks. Masters thesis, Dublin, National College of Ireland.
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Abstract
This paper evaluates machine learning (ML) and deep learning (DL) models for network traffic anomaly detection in IoT devices. Three models were tested: Support Vector Classifier (SVC), Convolutional Long Short Term Memory (ConvLSTM) and Extreme Gradient Boosting (XGBoost) Model. The motivation behind this project is to analyse, examine and evaluate machine learning and deep learning models and their effectiveness with respect to discovering and mitigating network anomalies in IoT frameworks. The accuracy for the XGBoost model is higher than the SVC and ConvLSTM models, with an accuracy of 99.98% as compared to 92.80% and 96.18% respectively. After each attack, the Inference system logs the result, sends email notifications when it detects an attack, and skips blocked IP addresses. Results demonstrate that XGBoost model has the ability to identify the DDoS attacks better, which would be promising for future network security applications in real environments.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Salahuddin, Jawad UNSPECIFIED |
| 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 T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things 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: | 28 Jul 2025 10:54 |
| Last Modified: | 28 Jul 2025 10:54 |
| URI: | https://norma.ncirl.ie/id/eprint/8256 |
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