TY - GEN
T1 - Optimized Cyber Attack Detection in IoT Networks Using Feature Selection and LightGBM
AU - Gaurav, Akshat
AU - Gupta, Brij B.
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The spread of IoT networks presents major security issues, especially with regard to identifying and reducing cyberattacks. In this context, we proposed an optimal LightGBM model for IoT network cyber attack detection. We used Random Forest classifier to find optimal features. Then LightGBM is trained on the selected features. After this the LightGBM model was tested against eleven conventional machine learning models. With the greatest accuracy (9 9. 8%), F1 score (9 9. 8%), precision (9 9. 8%), and recall (9 9. 8%), the LightGBM model clearly outperformed other machine learning models.
AB - The spread of IoT networks presents major security issues, especially with regard to identifying and reducing cyberattacks. In this context, we proposed an optimal LightGBM model for IoT network cyber attack detection. We used Random Forest classifier to find optimal features. Then LightGBM is trained on the selected features. After this the LightGBM model was tested against eleven conventional machine learning models. With the greatest accuracy (9 9. 8%), F1 score (9 9. 8%), precision (9 9. 8%), and recall (9 9. 8%), the LightGBM model clearly outperformed other machine learning models.
KW - Cyber Attack Detection
KW - Feature Selection
KW - IoT Security
KW - LightGBM
UR - http://www.scopus.com/inward/record.url?scp=85217852065&partnerID=8YFLogxK
U2 - 10.1109/WPMC63271.2024.10863747
DO - 10.1109/WPMC63271.2024.10863747
M3 - Conference contribution
AN - SCOPUS:85217852065
T3 - International Symposium on Wireless Personal Multimedia Communications, WPMC
BT - 27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024
T2 - 27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024
Y2 - 17 November 2024 through 20 November 2024
ER -