Detection of Flood and Brute force Attacks on IOT devices using Hybrid model approach

Singamsetty, Venkata Nithin Krishna (2024) Detection of Flood and Brute force Attacks on IOT devices using Hybrid model approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

The usage of IOT devices has increased immensely all over the world. IoT devices now being great targets for cyberattacks like brute force efforts and flood attacks, this explosion in IoT adoption has also brought vulnerabilities. Strong detection methods are required since these threats expose security, dependability of IoT networks. In order to detect these assaults on IOT devices, this study suggests a hybrid machine learning model that combines the advantage of various methods to provide high accuracy and real-time responsiveness.

The research is on combining the supervised and unsupervised learning methods to check the system and network traffic which makes the system power to detect malicious activity. Comparing solo models to combined models the hybrid combination performs better in all the metrics evaluation.

This study also assesses the model's scalability and adaptability in dynamic IoT contexts, emphasizing how well it can identify intricate attack patterns and changing threats. By offering a reliable, effective, and flexible way to reduce the dangers associated with brute force and flood assaults, the research helps to improve IoT security and eventually ensure safer IOT deployments in crucial applications.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Prior, 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 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:	28 Jul 2025 11:07
Last Modified:	28 Jul 2025 11:07
URI:	https://norma.ncirl.ie/id/eprint/8257

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