9th International Conference on ICT for Sustainable Development

Authors - Javed Shaikh, Vinay Yandrapalli, Nilesh T.Gole, Aditya Patil, Nilesh P.Bodne, Smitta Matte, Syed Umar
Abstract - The rapid growth of the internet and the telecommunications industry has led to a huge explosion of network size and data consistency. Therefore, when VANET grows and grows more and more in terms of transmission speed, network connectivity, security, and safety with the deployment of cutting applications, there will be major changes in wireless communication. In order to prevent external communications from being hacked, this study provides an intrusion detection system (IDS) for VANET that makes use of obfuscation. In this paper we proposed intrusion detection system for VANET which used the Attentive Interpretable Tabular Learning (Tab Net) architecture of deep learning. Tab Net model has characteristics of good interpretability and Fast training speed. we examined the existing machine and Deep learning framework and application in cyber security and VANET and PyTorch-Fast.ai is selected due to it is designed for CPU usage but because of training speed is high we changed data block fetching method from Tabular panda to NumPy array. Finally, from the evaluated metrics, we have proposed the best DNN design suitable for the IDS. With an accuracy of 98.12% and a False Alarm Rate (FAR) of 0.78 %.