TY - GEN
T1 - Enhancing IoT Botnet Detection
T2 - 2024 International Conference on Information Technology and Computing, ICITCOM 2024
AU - Saputro, Hartono
AU - Ahmad, Tohari
AU - Rachman Putra, Muhammad Aidiel
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The Internet of Things (IoT) revolution has increased the need for robust cybersecurity measures due to the increased vulnerability of interconnected devices to botnet attacks. Attack datasets are always imbalanced, which affects detection performance. This research investigates the effectiveness of Naive Bayes classifiers in detecting IoT botnets, focusing on the impact of SMOTE, ADASYN, and Borderline-SMOTE ensemble balancing techniques on classification performance. Using the IoT-23 dataset, this study systematically evaluates these techniques in terms of precision, recall, F1-score, and overall accuracy. Despite these challenges, SMOTE emerged as a relatively better balancing technique, with a high precision in class identification of 99.19%. The recall of 72.45% confirms its ability to recognize positive class instances, the F1-score of 79.13% shows an optimal balance between precision and recall, and the accuracy of 72.45% shows consistency. This study contributes valuable insights into enhancing cybersecurity for IoT networks through improved botnet detection methodologies.
AB - The Internet of Things (IoT) revolution has increased the need for robust cybersecurity measures due to the increased vulnerability of interconnected devices to botnet attacks. Attack datasets are always imbalanced, which affects detection performance. This research investigates the effectiveness of Naive Bayes classifiers in detecting IoT botnets, focusing on the impact of SMOTE, ADASYN, and Borderline-SMOTE ensemble balancing techniques on classification performance. Using the IoT-23 dataset, this study systematically evaluates these techniques in terms of precision, recall, F1-score, and overall accuracy. Despite these challenges, SMOTE emerged as a relatively better balancing technique, with a high precision in class identification of 99.19%. The recall of 72.45% confirms its ability to recognize positive class instances, the F1-score of 79.13% shows an optimal balance between precision and recall, and the accuracy of 72.45% shows consistency. This study contributes valuable insights into enhancing cybersecurity for IoT networks through improved botnet detection methodologies.
KW - IoT
KW - botnet
KW - machine learning
KW - network infrastructure
KW - network traffic
UR - https://www.scopus.com/pages/publications/85214662963
U2 - 10.1109/ICITCOM62788.2024.10762450
DO - 10.1109/ICITCOM62788.2024.10762450
M3 - Conference contribution
AN - SCOPUS:85214662963
T3 - Proceedings - 2024 International Conference on Information Technology and Computing, ICITCOM 2024
SP - 260
EP - 265
BT - Proceedings - 2024 International Conference on Information Technology and Computing, ICITCOM 2024
A2 - Chen, Hsing-Chung
A2 - Mashor, Mohd Yusoff Bin
A2 - Damarjati, Cahya
A2 - Jusman, Yessi
A2 - Alamsyah, Nurwahyu
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 August 2024 through 8 August 2024
ER -