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Detecting security breach using artificial neural network

Hyder, Mohammed Sharfuddin (2023) Detecting security breach using artificial neural network. Masters thesis, Dublin, National College of Ireland.

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Networks are a part of almost every important sector in the world today. The amount of data being transferred through these networks is huge, and it is important that this data be protected as it may be of high value to a number of companies and individuals. But these networks are highly vulnerable, and people with malicious intent are always on the lookout for ways to exploit these vulnerabilities. As breaches of networks are becoming very frequent, in order to identify and classify these data breaches or attacks, a system must be developed. Thus, a method to detect and classify the various types of network intrusions is suggested here. A model of an artificial neural network (ANN) will serve as the foundation for the system's development. Using the CSE-CIC-IDS 2022 dataset's data, the ANN will be trained. The technique known as Analysis of Variance (ANOVA) will be used to pick the dataset's most crucial properties. The dataset will need to be balanced because of the imbalance; hence the Synthetic Minority Oversampling Method (SMOTE) will be utilised. The technology will be put into use as a desktop programmed that can identify the kind of intrusion that has taken place on a network based.

Item Type: Thesis (Masters)
Ayala-Rivera, Vanessa
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources
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: Tamara Malone
Date Deposited: 05 Jan 2024 14:53
Last Modified: 05 Jan 2024 14:53

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