Detection of Security Vulnerabilities in IoT Devices using Advanced Deep Learning methods within Cloud Computing Framework

Raavi, Himavanth (2024) Detection of Security Vulnerabilities in IoT Devices using Advanced Deep Learning methods within Cloud Computing Framework. Masters thesis, Dublin, National College of Ireland.

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Abstract

The rapid proliferation of IoT devices, along with cloud computing capabilities, has made disruptive changes to modern industry by enabling seamless connectivity, scalabilities, and data processing. While this incorporation gave rise to many advancements, it, however, raised some of the most critical vulnerabilities from a security perspective, as IoT systems are becoming vulnerable to synthetic malware attacks. Traditional security mechanisms, usually static and rule-based, always have limitations in identifying and mitigating these complex threats, especially in real-time scenarios of dynamic IoT-cloud environments. This research proposed a deep learning solution to tackle problems with regard to malware detection in IoT systems hosted in the cloud environment. Upon implementing and evaluating three deep learning models based on accuracy, precision, recall, and F1-score among CNN, RNN, and auto encoders, we have identified CNN as the top model, consistently outperforming as compared to the other models across all metrics. This trained CNN model was deployed in a cloud-based web application running on an AWS EC2 instance for real-time monitoring and classification of network traffic. It comes with a user-friendly interface to allow the classification of traffic into benign or malicious in order to address the threats on time to the system administrators. The proposed framework successfully provides detection and handling of bulk IoT traffic and addresses important security challenges by leveraging the strength and availability of the cloud.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Kazmi, Aqeel UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing 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 Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	16 Jul 2025 10:54
Last Modified:	16 Jul 2025 10:54
URI:	https://norma.ncirl.ie/id/eprint/8143

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