Abstract
Vehicular ad-hoc networks (VANETs) are core components of the cooperative intelligent transportation system (C-ITS). Vehicles communicate with each other to obtain traffic conditions on the current road segment by broadcasting authenticated safety messages using their digital certificates. Although this method protects the system against external threats, it is ineffective when faced with internal adversaries who possess legal certificates. Consequently, an increasing number of researchers have focused on intrusion detection (misbehavior detection) technology. VeReMi and its extension version are the only public misbehavior datasets of VANETs in its field, allowing researchers to compare their studies with those of others. We note that denial of service (DoS) attacks in these datasets are insufficiently comprehensive. As a result, we designed a more complete dataset than existing datasets by implementing multiple attacks, including different types of distributed denial of service (DDoS) attacks. We present the detection results of some machine learning algorithms on our proposed dataset. These results indicate that our dataset can be utilized as a reference for future studies to evaluate different detection methods.
The work was supported by the National Natural Science Foundation of China (61872001), the Excellent Youth Foundation of Anhui Scientific Committee (2108085J31), and the Special Fund for Key Program of Science and Technology of Anhui Province, China (202003A05020043).
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Zhong, H., Yang, F., Wei, L., Zhang, J., Gu, C., Cui, J. (2022). Dataset for Evaluation of DDoS Attacks Detection in Vehicular Ad-Hoc Networks. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_21
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