Honeypots and the Use of AI in keeping the IoT Systems Secure

Goundla, Venkat Goud (2024) Honeypots and the Use of AI in keeping the IoT Systems Secure. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (2MB) | Preview
Preview	PDF (Configuration Manual) Download (947kB) | Preview

Abstract

Honeypots play a crucial role in a comprehensive cybersecurity strategy by offering early detection, useful intelligence, and improved reaction capabilities. They also increase the overall security posture of a business. This study centers on implementing a sophisticated security solution for IoT using honeypots. The honeypots are monitored using the most efficient machine learning model to identify illegal access and deploy honeypots in a dynamic manner. The system utilizes models such as LightGBM, which have shown to be highly accurate and efficient, to accurately detect threats while decreasing the occurrence of false positives. This strategy enhances memory efficiency by selectively activating honeypots just in high-confidence threat scenarios, hence minimizing superfluous resource utilization. Machine learning integration improves the ability to identify and respond to threats in real-time, offering a security solution that is adaptable, effective, and strong, specifically designed for critical IoT settings. This solution is then deployed in the real time monitoring of the iOT devices.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Mustafa, Raza Ul UNSPECIFIED
Uncontrolled Keywords:	Cybersecurity; Machine Learning; LGBM; Access Monitoring
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence 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:	17 Jul 2025 14:52
Last Modified:	17 Jul 2025 14:52
URI:	https://norma.ncirl.ie/id/eprint/8173

Actions (login required)

View Item