TY - GEN
T1 - Efficient Cyber Attack Detection in Wireless Sensor Networks Using ANOVA F-Test and XGBoost
AU - Gaurav, Akshat
AU - Gupta, Brij B.
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Wireless Sensor Networks (WSNs) are indispensable in many different applications. Hence, reliability and security of these systems depend on effective detection systems. In this work, we use XGBoost for classification and the ANOVA F-Test for feature selection to provide a model for cyber threat detection in WSNs. We picked the best features using ANOVA F-Test. Particularly excelling in identifying TDMA and Blackhole attack types, our XGBoost model obtained a 90% accuracy, surpassing Logistic Regression, SVM, Gradient Boosting, and Catboost in recall and F1-score across most attack classes.
AB - Wireless Sensor Networks (WSNs) are indispensable in many different applications. Hence, reliability and security of these systems depend on effective detection systems. In this work, we use XGBoost for classification and the ANOVA F-Test for feature selection to provide a model for cyber threat detection in WSNs. We picked the best features using ANOVA F-Test. Particularly excelling in identifying TDMA and Blackhole attack types, our XGBoost model obtained a 90% accuracy, surpassing Logistic Regression, SVM, Gradient Boosting, and Catboost in recall and F1-score across most attack classes.
KW - ANOVA F-Test
KW - Cyber Attack Detection
KW - Feature Selection
KW - Wireless Sensor Networks (WSNs)
KW - XGBoost
UR - https://www.scopus.com/pages/publications/105004559853
U2 - 10.1109/INDICON63790.2024.10958271
DO - 10.1109/INDICON63790.2024.10958271
M3 - Conference contribution
AN - SCOPUS:105004559853
T3 - 2024 IEEE 21st India Council International Conference, INDICON 2024
BT - 2024 IEEE 21st India Council International Conference, INDICON 2024
T2 - 21st IEEE India Council International Conference, INDICON 2024
Y2 - 19 December 2024 through 21 December 2024
ER -