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Olaniyan, Oluwaseun Abiola (2021) Evaluation of Machine Learning and Deep Learning Algorithms on Network Intrusion Detection. Masters thesis, Dublin, National College of Ireland.
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Abstract
Due to the introduction of the devices for networking with the fast internet development in earlier years, the safety of the networks has developed to be important in this contemporary age. (NIDS) Network Intrusion Detection Systems are used in identifying unapproved, unacquainted and traffic that is suspicious through networks. This project pursues the combination of the two commonly known network intrusion detection types that are, misuse detection and anomaly detection through the design of a hybrid model that classifies a network traffic first either as benign or intrusive. When the traffic is established as intrusive, the model additionally detects the intrusive traffic category travelling throughout the network. Furthermore, the research proposes deep learning and machine learning algorithm usage in determining the quickest and utmost precise algorithm for network intrusions detection.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Machine learning; Anomaly detection; NIDS; deep learning; misuse detection |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security |
| Divisions: | School of Computing > Master of Science in Cyber Security |
| Depositing User: | Clara Chan |
| Date Deposited: | 01 Nov 2021 12:04 |
| Last Modified: | 01 Nov 2021 12:04 |
| URI: | https://norma.ncirl.ie/id/eprint/5121 |
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