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Mohandass, Sabareesan (2024) Addressing Cloud Security Challenges using AI-Driven IoT Intrusion Detection Systems with UQ-IDS Dataset. Masters thesis, Dublin, National College of Ireland.
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Abstract
Cloud computing and the Internet of Things (IoT) have expanded the attack surface and led to the introduction of new cybersecurity challenges, necessitating improved security measures to protect against the increased risk. However, traditional Intrusion Detection Systems (IDS) have been unable to keep up with use of evolving threats. Issues with traditional IDSs include high false-positive rates and poor ability to detect new types of attacks by utilizing sophisticated techniques. To overcome these challenges in this research, we propose a Conv-LSTM hybrid model that takes the strengths of Convolutional Neural Networks (CNN) in identifying patterns and the strengths of Long Short-Term Memory (LSTM) networks in processing sequential data. By using a hybrid architectural approach to the problem, we can improve both the accuracy of detection and reduce false positives. This research also includes an implementation of a web application that can be used in real time to present alerts to administrators through a friendly web interface. The novelty aspect of this research is the implementation of hybrid model ConvLSTM, which is the most accurate model for detecting the anomalies from the system as compared to CNN and LSTM. Also, the implementation of web application in the cloud environment, offers a realistic and industry valid framework for a scalable and efficient cybersecurity solution for the modern network infrastructure.
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