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Network Intrusion Detection System using Ensemble Learning

Patil, Samruddhi (2020) Network Intrusion Detection System using Ensemble Learning. Masters thesis, Dublin, National College of Ireland.

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Cyber-attacks have always been a great threat to the IT sector. Network intrusion also known as APT is a major concern to all the global scale industries and government sectors. APT in a brief refers to persistent long term multi-stage attack whose main goal is to infiltrate the target network and gather sensitive information anonymously which can lead to great financial losses. These attacks are generally launched with an intention to steal data rather than to cause damage. This thesis aims at developing a system that can detect an APT attack using the concept of machine learning. The thesis aims at boosting the overall accuracy of the prediction by making use of an ensemble learning algorithm. An ensemble-based algorithm is a hybrid algorithm that is formed by a combination of two or more sets of models.
Keywords: Network Intrusion Detection System, Ensemble Learning, Machine Learning, XGBoost, KNN, Logistic regression, Stacking, Hybrid Algorithm, Classifiers

Item Type: Thesis (Masters)
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: Dan English
Date Deposited: 27 Jan 2021 17:56
Last Modified: 27 Jan 2021 17:56

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