Big Cyber Security Data Analysis with Apache Mahou

Authors

Omotola Adekanbmi, Georgia Southern UniversityFollow
Hayden Wimmer, Georgia Southern UniversityFollow
Jongyeop Kim, Georgia Southern UniversityFollow

Document Type

Conference Proceeding

Publication Date

6-30-2023

Publication Title

IEEE/ACIS 20th International Conference on Software Engineering Research, Management and Applications (SERA) Proceedings

DOI

10.1109/SERA54885.2022.9806807

Abstract

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 Naïve 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 F1- Score for a comparative analysis of the various machine learning classifiers.

Comments

Georgia Southern University faculty member, Hayden Wimmer and Jongyeop Kim co-authored Big Cyber Security Data Analysis with Apache Mahou.

Recommended Citation

Adekanbmi, Omotola, Hayden Wimmer, Jongyeop Kim. 2023. "Big Cyber Security Data Analysis with Apache Mahou." IEEE/ACIS 20th International Conference on Software Engineering Research, Management and Applications (SERA) Proceedings: 83-90: IEEE Xplore. doi: 10.1109/SERA54885.2022.9806807
https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/174

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