TY - GEN
T1 - Multiclass Imbalance Resampling Techniques for Network Intrusion Detection
AU - Saharuna, Zawiyah
AU - Ahmad, Tohari
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Security datasets often exhibit significant imbalances that can introduce bias during model training, diminish sensitivity to actual attacks, and lead to a substantial number of false negatives, potentially overlooking real threats. This is particularly evident in the highly skewed distribution of the UNSW-NB18 Bot-IoT dataset. To mitigate these issues, this study proposes implementing either Random Oversampling (ROS) or Synthetic Minority Oversampling (SMOTE) in conjunction with five ensemble algorithms to develop models for predicting intrusions in the Internet of Things networks. The results show that incorporating these methods with ensemble learners significantly improves model accuracy by 1 % to 4 % across the four algorithms compared to their absence. In addition, there were dramatic increases in precision, recall, and F1-score, achieving values between 95% and 100%.
AB - Security datasets often exhibit significant imbalances that can introduce bias during model training, diminish sensitivity to actual attacks, and lead to a substantial number of false negatives, potentially overlooking real threats. This is particularly evident in the highly skewed distribution of the UNSW-NB18 Bot-IoT dataset. To mitigate these issues, this study proposes implementing either Random Oversampling (ROS) or Synthetic Minority Oversampling (SMOTE) in conjunction with five ensemble algorithms to develop models for predicting intrusions in the Internet of Things networks. The results show that incorporating these methods with ensemble learners significantly improves model accuracy by 1 % to 4 % across the four algorithms compared to their absence. In addition, there were dramatic increases in precision, recall, and F1-score, achieving values between 95% and 100%.
KW - Imbalanced Data
KW - IoT
KW - Network Intrusion Detection
KW - Network Security
KW - Resampling
UR - http://www.scopus.com/inward/record.url?scp=85207475410&partnerID=8YFLogxK
U2 - 10.1109/ICSCC62041.2024.10690582
DO - 10.1109/ICSCC62041.2024.10690582
M3 - Conference contribution
AN - SCOPUS:85207475410
T3 - 2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
SP - 450
EP - 454
BT - 2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Smart Computing and Communication, ICSCC 2024
Y2 - 25 July 2024 through 27 July 2024
ER -