Big Cyber Security Data Analysis with Apache Mahou
Omotola Adekanbmi, Hayden Wimmer, Jongyeop Kim · 2022
Machine learning classifiers are known algorithms used to classify network intrusion detection due to the drastic growth of data, new tools are being required to handle such a large amount of data within a short time frame. In this Paper, we present a Model using the Apache Mahout Framework to train machine learning classifiers Random Forest (RF), Logistic Regression (LR), and Naive Bayes (NB) on CSE-CIC-IDS2018 dataset using Chi-Square and ANOVA f-test filter-based feature selection technique on an Apache Hadoop Framework. The performance of classifiers is measured in terms of Accuracy, Kappa, Precision, Recall, and Fl-Score for a comparative analysis of the various machine learning classifiers.