“A Computationally Efficient Intrusion Detection System for VANETs Using Tabular Learning Techniques”

This is unique as it is the first time TabNet DL has been used for VANET security. We have fully evaluated the VANET architecture, including security and privacy aspects. As part of our research, we have narrowed the scope of this research to securing network data. We introduced a heavy-duty design for IDS in the VANET network. The TabNet model was proposed, which produced exceptional results, surpassing previous works in the literature. In addition, unlike previous studies, we used the CIC-CSE-IDS2018 dataset for the experiment. As shown in result and discussion section. All layer IDS outperforms all author known IDS classifiers in terms of accuracy and FAR, with 98.12 % and 0.78%, respectively.

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