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A Comparative Evaluation of Machine Learning Models and EDA through Tableau Using CICIDS2017 Dataset

Bangari, Abhay Singh (2023) A Comparative Evaluation of Machine Learning Models and EDA through Tableau Using CICIDS2017 Dataset. Masters thesis, Dublin, National College of Ireland.

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Machine learning is utilized globally in network security, but computers need time to learn. Machine learning can identify many hacker attacks that humans cannot. Business intelligence and machine learning are being studied to strengthen network systems. The research topic is briefly covered in the study. Academic articles on the study topic are examined, and effective research methods are described. In this work, Python is used as a medium to build popular algorithms and Tableau for visualizations. Machine learning models like AdaBoost, XGBoost, Random Forest, Decision Tree, KNearest Neighbor, and Linear Discriminant Analysis. The Canadian Institute for Cybersecurity’s CICIDS2017 dataset is used for in-depth analysis. Performance metrics for all the algorithms are computed, with the help of accuracy, F1-score, precision, and recall. The following investigation revealed that XGBoost is the better-performing algorithm. The random forest model is the best-performing model in terms of accuracy (98.38%), and F1-score (98.37%). Contrary to others, AdaBoost and linear discriminant analysis models have been proven to be less effective at preventing intrusions. On testing with different numbers of features in the random forest, it is discovered that the model with 35 features improves its performance.

Item Type: Thesis (Masters)
Muntean, Cristina Hava
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 > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 17 May 2023 10:35
Last Modified: 17 May 2023 10:35

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