Deep learning-based intrusion detection system in the internet of things

Mishra, Abinash (2024) Deep learning-based intrusion detection system in the internet of things. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (775kB) | Preview
Preview	PDF (Configuration Manual) Download (771kB) | Preview

Abstract

This study aims to create and assess a deep learning-based intrusion detection system(IDS) for enhancing security in Internet of Things (IoT) environments. Goals include investigating DL algorithms to enhance IDS efficiency and accuracy in IoT, identifying effective approaches for detecting and mitigating various cyberattacks, and addressing how deep learning-based IDS architecture handles dynamic and heterogeneous IoT network traffic and devices. The study also explores strategic planning for robust DL approaches to mitigate threats in IoT devices and systems and examines architectural frameworks of DL models that can be implemented to identify and mitigate threats in IoT systems and devices.

Item Type:	Thesis (Masters)
Supervisors:	Name Email McLaughlin, Eugene 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:	30 Jul 2025 11:22
Last Modified:	30 Jul 2025 11:22
URI:	https://norma.ncirl.ie/id/eprint/8339

Actions (login required)

View Item