Optimizing Real-Time DDoS Detection with Autoencoders for Enhanced Cybersecurity

Murali, Raj Bharath (2024) Optimizing Real-Time DDoS Detection with Autoencoders for Enhanced Cybersecurity. Masters thesis, Dublin, National College of Ireland.

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Abstract

The emerging threats of Distributed Denial-of-Service (DDoS) attacks and issues with previous models have raised concerns about the security of cyberspace. If critical systems are compromised, the results could range from failure to life-threatening situations. Previous strategies have primarily been designed for traditional machine learning, and rule-based methods are not as effective for identifying new attacks. This research utilized deep learning algorithms with an emphasis on autoencoders to solve the DDoS detection challenge. Through our analysis, we discovered that autoencoders are more accurate and have higher precision, recall, and F1-score than the CNN and RNN models we tested. The principal innovation of our work was creating a real-time web application that incorporates the autoencoder model, which can be used in a live environment to automatically detect and defend against DDoS incidents. We feel that our contribution is unique and that it will be welcomed by the community, as it offers a scalable approach that can be applied to a variety of fields and can be used to test future detection strategies.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Pantridge, Michael 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 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 11:45
Last Modified:	30 Jul 2025 11:45
URI:	https://norma.ncirl.ie/id/eprint/8343

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