Securing Cloud Environments Through Real-Time Network Monitoring System for Detecting Network Attacks using Advanced Deep Learning Methods

Vanga, Venkateshwarlu (2023) Securing Cloud Environments Through Real-Time Network Monitoring System for Detecting Network Attacks using Advanced Deep Learning Methods. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the domain of cloud computing, the widespread adoption of shared resources and remote services increases security vulnerabilities, particularly from hostile data packets threatening data confidentiality, integrity, and availability. As cloud computing becomes integral to business operations, organizations must adopt advanced strategies and technologies to mitigate these risks. This study explores deep learning algorithms for anomaly detection in cloud-based systems, where anomalies signal potential threats to data security and system integrity. Focusing on the effectiveness of Graph Neural Network (GNN), Autoencoder, and Recurrent Neural Network (RNN), the research adopts a comprehensive methodology, beginning with anomaly data collection from reliable sources. Preprocessing steps ensure data quality and balance, while feature engineering techniques like label encoding and principal component analysis (PCA) optimize data representation and reduce dimensionality for enhanced efficiency. Among the evaluated algorithms, the Autoencoder emerges as most effective in anomaly detection within cloud environments, achieving 99.99% accuracy. Its superior sensitivity and specificity effectively minimize false positives to 0.001, accurately identifying anomalies and contributing significantly to automated anomaly detection software development. This research advances the field of anomaly detection in cloud computing, providing insights into the relative merits of various deep learning approaches and their practical applications in ensuring cloud data security.

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:	11 Apr 2025 10:48
Last Modified:	11 Apr 2025 10:48
URI:	https://norma.ncirl.ie/id/eprint/7420

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