An Enhanced Online K-Means Algorithm for Flooding Attacks Detection in Vehicular Networks

Vehicle ad-hoc networks represent a promising technology aimed at providing more efficient, safer, and partially autonomous transportation through vehicle-to-vehicle and infrastructure communications. The success of these networks heavily relies on the integrity and correctness of transmitted information. One of the most significant threats to communication is Denial of Service attacks, which can disrupt the system by overloading hardware modules. To effectively identify these attacks, we propose a modified version of the online K-Means algorithm. In our method, we aim to mitigate the impact of outliers on each adjusted centroid by incorporating an outlier detection method. In this paper, we compare the performance of the model by incorporating 4 different outlier detection methods in terms of execution time, accuracy and required buffer sizes. Additionally, we address a common oversight in accuracy calculations when using buffers for streaming data. Our study highlights the impact of data loss on accuracy. We also suggest several techniques to enhance the model in detecting DoS Attacks in terms of accuracy, precision and F1-score.