TY - GEN
T1 - AI-Driven Intelligent Attack Detection for IoT Networks Using Big Data and Machine Learning
AU - Gaurav, Akshat
AU - Attar, Razaz Waheeb
AU - Arya, Varsha
AU - Castiglione, Arcangelo
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - With the exponential growth of IoT networks, ensuring robust security has become increasingly critical, as these systems are vulnerable to various cyberattacks. Traditional methods often struggle to handle the massive data generated by IoT devices. This paper introduces an AI-driven, big data approach to intelligent attack detection for IoT networks. Utilizing the NSLKDD dataset, we employed PySpark for preprocessing and chi-square-based feature selection to identify the 15 most significant features, optimizing performance and reducing computational costs. The proposed model, based on XGBoost, achieved outstanding classification results with 98.93% accuracy, and precision, recall, and F1-score approaching 99%. Comparative analysis against models like Random Forest and LightGBM confirmed its effectiveness, providing a scalable, accurate solution for IoT security.
AB - With the exponential growth of IoT networks, ensuring robust security has become increasingly critical, as these systems are vulnerable to various cyberattacks. Traditional methods often struggle to handle the massive data generated by IoT devices. This paper introduces an AI-driven, big data approach to intelligent attack detection for IoT networks. Utilizing the NSLKDD dataset, we employed PySpark for preprocessing and chi-square-based feature selection to identify the 15 most significant features, optimizing performance and reducing computational costs. The proposed model, based on XGBoost, achieved outstanding classification results with 98.93% accuracy, and precision, recall, and F1-score approaching 99%. Comparative analysis against models like Random Forest and LightGBM confirmed its effectiveness, providing a scalable, accurate solution for IoT security.
KW - Attack Detection
KW - Big Data Analytics
KW - IoT Security
KW - Machine Learning
UR - https://www.scopus.com/pages/publications/105018472629
U2 - 10.1109/ICC52391.2025.11161110
DO - 10.1109/ICC52391.2025.11161110
M3 - Conference contribution
AN - SCOPUS:105018472629
T3 - IEEE International Conference on Communications
SP - 2599
EP - 2604
BT - ICC 2025 - IEEE International Conference on Communications
A2 - Valenti, Matthew
A2 - Reed, David
A2 - Torres, Melissa
T2 - 2025 IEEE International Conference on Communications, ICC 2025
Y2 - 8 June 2025 through 12 June 2025
ER -