Big Data Approach For IoT Botnet Traffic Detection Using Apache Spark Technology
Document Type
Conference Proceeding
Publication Date
4-18-2023
Publication Title
IEEE Annual Computing and Communication Workshop and Conference (CCWC) Proceedings
DOI
10.1109/CCWC57344.2023.10099385
Abstract
In recent years, numerous machine learning classifiers have been applied to improve network infiltration. Due to the exponential growth of data, new technologies are needed to handle such massive amounts of data in a timely manner. The machine learning classifiers are trained on datasets for intrusion detection. In this study, we used the feature selection technique to choose the best dataset characteristics for machine learning and then performed binary classification to distinguish the intrusive traffic from the normal one using four machine learning algorithms, including Decision Tree, Support Vector Machine, Random Forest, and Naive Bayes in the UNSW-NB15 data set on Apache Spark framework. The performance of classifiers is evaluated in terms of accuracy, precision, recall, and F1-score for a comparative analysis of the various machine learning classifiers.
Recommended Citation
Oluwatomisin, Arokodare, Hayden Wimmer, Jie Du.
2023.
"Big Data Approach For IoT Botnet Traffic Detection Using Apache Spark Technology."
IEEE Annual Computing and Communication Workshop and Conference (CCWC) Proceedings: IEEE Xplore.
doi: 10.1109/CCWC57344.2023.10099385
https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/162
Comments
Georgia Southern University faculty member, Hayden Wimmer co-authored Big Data Approach For IoT Botnet Traffic Detection Using Apache Spark Technology.