Intrusion Detection in Cloud Environments using Hybrid Deep Learning

Rana, Kunal (2024) Intrusion Detection in Cloud Environments using Hybrid Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

In todays modern word ensuring strong security measures against network intrusions is essential for cloud computing. This study investigates the use of deep learning and machine learning approaches for intrusion detection to improve cloud environment security. It looks at how well different models—such as Bidirectional LSTM, Decision Trees, Long-Short Term Memory (LSTM), and Logistic Regression— identify and categorize network intrusions. These models are assessed using accuracy, recall, and F1 score metrics on the UNSW-NB15 dataset. The study also explores how to pick features and how to employ autoencoders to improve model performance. The outcomes provide important insights into the potential real-world applications of each algorithm by highlighting its advantages and disadvantages.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Gupta, Shaguna 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 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:	03 Jun 2025 14:00
Last Modified:	03 Jun 2025 14:00
URI:	https://norma.ncirl.ie/id/eprint/7728

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