TY - GEN
T1 - Big Data Approach For IoT Botnet Traffic Detection Using Apache Spark Technology
AU - Arokodare, Oluwatomisin
AU - Wimmer, Hayden
AU - Du, Jie
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Apache Spark
KW - big data
KW - intrusion detection system
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85156200166&partnerID=8YFLogxK
U2 - 10.1109/CCWC57344.2023.10099385
DO - 10.1109/CCWC57344.2023.10099385
M3 - Conference article
AN - SCOPUS:85156200166
T3 - 2023 IEEE 13th Annual Computing and Communication Workshop and Conference, CCWC 2023
SP - 1260
EP - 1266
BT - 2023 IEEE 13th Annual Computing and Communication Workshop and Conference, CCWC 2023
A2 - Paul, Rajashree
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2023
Y2 - 8 March 2023 through 11 March 2023
ER -