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Tavares de Sá, Felipe (2022) Detection of De-authentication attack in IEEE 802.11 Networks: A Machine Learning Strategy. Masters thesis, Dublin, National College of Ireland.
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Abstract
As technology evolves, new cyberattacks are emerging, creating real danger for users. IoT devices and their vulnerability have made them an easy target for attacks. Even with the dynamic nature of IoT networks, there is difficulty in developing rule-based security systems. This scenario becomes an invitation to employ machine learning techniques. New ways of protection are being studied every day. This research report presents a denial of service (DoS) analysis in a home Wi-Fi network. The environment is a residence with several IoT devices connected to the internet using the 802.11 Wi-Fi protocol. The threat scenario is the Deauthentication attack. The method for Deauth (DoS) classification uses a dataset made up of malicious and legitimate network traffic that was captured using a Raspberry Pi 4 and is based on Random Forest (RF), XGBoost, Logistic Regression (LR) and Decision Tree (DT) algorithms. De-authentication, (DoS) attack is classified with an F1-score of 100% by the XGBoost, RF, and DT models that were developed for this research.
| Item Type: | Thesis (Masters) |
|---|---|
| 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: | Tamara Malone |
| Date Deposited: | 05 Jan 2023 16:30 |
| Last Modified: | 07 Mar 2023 12:05 |
| URI: | https://norma.ncirl.ie/id/eprint/6067 |
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