Tools
Tools
Saravanan, Ranjith Kumar (2024) Optimizing Network Security: Performance Analysis of Neural Network Models for Intrusion Detection. Masters thesis, Dublin, National College of Ireland.
Preview |
PDF (Master of Science)
Download (1MB) | Preview |
Preview |
PDF (Configuration Manual)
Download (730kB) | Preview |
Abstract
Since cybersecurity threats became more intelligent, defending against them required high-powered mechanisms to maintain network integrity. As such, the report devised potent intrusion detection models using the NSL-KDD dataset. The dataset included various vital threats and hazards, such as the back, neptune, portsweep, and smurf alongside normal traffic. The effectiveness of the Artificial Neural Networks and Long Short-Term Memory LSTM models was tested on these samples. The results demonstrated a validation accuracy of 66.70% for the ANN and 99.10% for the LSTM, offering a novel neural network approach to these major types of malicious shellcode, rather than depending on previous signature-based methods. The developed approach enabled outstanding accuracy and far fewer false positives than before, providing an improvement in cybersecurity.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Hafeez, Khadija 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: | 31 Jul 2025 08:36 |
| Last Modified: | 31 Jul 2025 08:36 |
| URI: | https://norma.ncirl.ie/id/eprint/8365 |
Actions (login required)
 |
View Item |