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Shahul Hameed, Shahna (2024) DeepDefend: Optimized Multi-Model Approach for Network Intrusion Detection Using Deep Learning and IoT Security Enhancement. Masters thesis, Dublin, National College of Ireland.
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Abstract
Network intrusion detection has become an important component of current cybersecurity techniques due to the growing frequency of cyberattacks in both conventional and Internet of Things-based network environments. However, current intrusion detection systems (IDS) frequently have high false-positive rates due to limitations in accuracy, adaptability, and the capacity to detect minority attack classes. In order to overcome these obstacles, this study suggests a new hybrid framework that combines advanced deep learning (DL) models with conventional machine learning (ML) techniques. The suggested method ensures strong generalization and adaptability across various network environments by utilizing a variety of datasets, such as UNSW-NB15, NSL-KDD, Cyber Intrusion and ToN IoT. The Perceptual Pigeon Galvanized Optimization (PPGO) method which is especially used to optimize Long Short-Term Memory (LSTM) models for improved IoT security, lies at the core of the suggested solution. In order to achieve balanced performance across a variety of attack vectors, this structure places to strong focus on lowering false-positives rates while also greatly increasing the detection accuracy of minority attack classes. Significant improvements in detection accuracy of upto 98.5% on UNSW-NB15 and 97.8% on NSK-KDD datasets, false-positive reduction, and flexibility to different network configurations are shown by exploratory results from experiments. The results help in the creation of a scalable and robust intrusion detection system that addresses the constantly evolving and complex nature of modern cybersecurity risks. This study has the ability to greatly improve network security across business and IoT-based systems by offering an integrated and efficient solution, paving the way to more secure digital ecosystems.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Zahoor, Sheresh 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 Artificial Intelligence |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 20 Jun 2025 10:32 |
| Last Modified: | 20 Jun 2025 10:32 |
| URI: | https://norma.ncirl.ie/id/eprint/7967 |
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