TY - GEN
T1 - Analysis of Machine Learning Algorithms for DDoS Attack Detection in Connected Cars Environment
AU - Attar, Ali E.L.
AU - Wehby, Ayoub
AU - Chbib, Fadlallah
AU - Mehrez, Hassane Aissaoui
AU - Fadlallah, Ahmad
AU - Hachem, Joel
AU - Khatoun, Rida
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Cyberattacks against the Internet of Vehicles (IoV) will continue to evolve as the industry continues to adopt new and advanced connected technologies. These advances should result in a complex ecosystem that integrates different technologies (5G, 6G, Cloud, IoT, etc.) and presents a large attack surface. Denial of service (DoS) attacks are among the most dangerous attacks against connected vehicles, where an attacker overwhelm the network with random generated messages. An effective detection of the occurrence of such attacks is a key step for any defense scheme. Machine-Learning (ML) and Deep Learning (DL) algorithms have attracted a lot of attention in the literature for DoS detection. However, there is no comprehensive comparative analysis of their efficiency in the IoV context. In this paper, we study the detection performance of several classification algorithms such as Decision Tree, Random Forest, XGBoost, AdaBoost, Logistic Regression, Support Vector Machine (SVM), Naive Bayes, and K-nearest neighbors to differentiate normal CAM messages from flooding CAM ones launched by the vehicular bot in Vehicle-to-Infrastructure (V2I) environment. The obtained results demonstrate that XGBoost, Random Forest, and SVM algorithms have a very high detection precision.
AB - Cyberattacks against the Internet of Vehicles (IoV) will continue to evolve as the industry continues to adopt new and advanced connected technologies. These advances should result in a complex ecosystem that integrates different technologies (5G, 6G, Cloud, IoT, etc.) and presents a large attack surface. Denial of service (DoS) attacks are among the most dangerous attacks against connected vehicles, where an attacker overwhelm the network with random generated messages. An effective detection of the occurrence of such attacks is a key step for any defense scheme. Machine-Learning (ML) and Deep Learning (DL) algorithms have attracted a lot of attention in the literature for DoS detection. However, there is no comprehensive comparative analysis of their efficiency in the IoV context. In this paper, we study the detection performance of several classification algorithms such as Decision Tree, Random Forest, XGBoost, AdaBoost, Logistic Regression, Support Vector Machine (SVM), Naive Bayes, and K-nearest neighbors to differentiate normal CAM messages from flooding CAM ones launched by the vehicular bot in Vehicle-to-Infrastructure (V2I) environment. The obtained results demonstrate that XGBoost, Random Forest, and SVM algorithms have a very high detection precision.
KW - Connected cars
KW - classification
KW - cybersecurity
KW - machine learning
U2 - 10.1109/MobiSecServ58080.2023.10329152
DO - 10.1109/MobiSecServ58080.2023.10329152
M3 - Conference contribution
AN - SCOPUS:85181776128
T3 - Proceedings of the 2023 8th International Conference on Mobile and Secure Services, MobiSecServ 2023
BT - Proceedings of the 2023 8th International Conference on Mobile and Secure Services, MobiSecServ 2023
A2 - Urien, Pascal
A2 - Piramuthu, Selwyn
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Mobile and Secure Services, MobiSecServ 2023
Y2 - 4 November 2023 through 5 November 2023
ER -