TY - GEN
T1 - Towards Robust Internet of Vehicles Security
T2 - 18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024
AU - Zhu, Liehuang
AU - Bilal, Awais
AU - Sharif, Kashif
AU - Li, Fan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - In the evolving landscape of the Internet of Vehicles (IoV), ensuring robust security at the edge of the network is paramount. This study addresses the critical need for robust security in edge computing environments within the IoV. We conduct an in-depth evaluation of a wide range of machine learning algorithms - including Random Forest, Gradient Boosting, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Decision Trees, AdaBoost, Logistic Regression, and XGBoost - for cyber-attack classification in IoV systems. Utilizing the ML-EdgeIIoT-dataset, we assess these algorithms against key metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Our findings reveal that ensemble methods, particularly Gradient Boosting and XGBoost, demonstrate superior performance in accurately detecting IoV cyber threats, effectively balancing computational demands. The study highlights the importance of strategically selecting algorithms to meet the specific security needs of the dynamic IoV environment. The results not only enhance the current understanding of IoV security but also pave the way for future research to develop adaptive, efficient, and precise security mechanisms for real-time IoV applications.
AB - In the evolving landscape of the Internet of Vehicles (IoV), ensuring robust security at the edge of the network is paramount. This study addresses the critical need for robust security in edge computing environments within the IoV. We conduct an in-depth evaluation of a wide range of machine learning algorithms - including Random Forest, Gradient Boosting, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Decision Trees, AdaBoost, Logistic Regression, and XGBoost - for cyber-attack classification in IoV systems. Utilizing the ML-EdgeIIoT-dataset, we assess these algorithms against key metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Our findings reveal that ensemble methods, particularly Gradient Boosting and XGBoost, demonstrate superior performance in accurately detecting IoV cyber threats, effectively balancing computational demands. The study highlights the importance of strategically selecting algorithms to meet the specific security needs of the dynamic IoV environment. The results not only enhance the current understanding of IoV security but also pave the way for future research to develop adaptive, efficient, and precise security mechanisms for real-time IoV applications.
KW - Attack Classification
KW - Edge Computing
KW - Internet of Vehicles
KW - IoV Security
KW - Lightweight Machine Learning
KW - Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=85210174561&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-71470-2_7
DO - 10.1007/978-3-031-71470-2_7
M3 - Conference contribution
AN - SCOPUS:85210174561
SN - 9783031714696
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 78
EP - 89
BT - Wireless Artificial Intelligent Computing Systems and Applications - 18th International Conference, WASA 2024, Proceedings
A2 - Cai, Zhipeng
A2 - Takabi, Daniel
A2 - Guo, Shaoyong
A2 - Zou, Yifei
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 21 June 2024 through 23 June 2024
ER -