Deep Learning Empowered Intrusion Detection: Unmasking the Prevention Concept

Roy, Roshni (2023) Deep Learning Empowered Intrusion Detection: Unmasking the Prevention Concept. Masters thesis, Dublin, National College of Ireland.

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Abstract

The project aims to enhance the effectiveness of intrusion detection systems through the integration of advanced deep learning techniques. The project focuses on evaluating the efficacy of artificial neural network (ANN) and Gated Recurrent Units (GRU) algorithms in intrusion detection using the CICIDS2017 dataset, which comprises five classes: BENIGN, Brute Force, DDoS, DoS, and PortScan. Leveraging the power of deep learning, it enhances the accuracy and efficiency of intrusion detection systems by employing advanced neural network architectures. The ANN and GRU models are trained on the labeled dataset to learn patterns associated with various network intrusions, and their performance is meticulously assessed through comprehensive evaluation metrics. The research contributes to the field of cybersecurity by shedding light on the potential of deep learning techniques for unmasking intrusion attempts and bolstering prevention strategies. Results obtained from this study not only advance the understanding of the application of ANN and GRU in intrusion detection but also provide valuable insights into the practical implications of employing these models for robust network security. These five types of targets were classified, and the performance of artificial neural networks (ANN) and gated recurrent units (GRU) was evaluated. The results demonstrated that ANN achieved an accuracy of 95.47%, while GRU surpassed with an impressive accuracy of 99.10%. Notably, from both models, GRU emerged as the superior performer, providing the best results in target classification.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Pantridge, 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 > Algebra > Algorithms > Computer algorithms 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 Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	22 Apr 2025 14:20
Last Modified:	22 Apr 2025 14:20
URI:	https://norma.ncirl.ie/id/eprint/7461

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