A Hybrid Data-driven Model for Intrusion Detection in VANET

Nowadays, VANET (Vehicular Ad-hoc NETwork) has gained increasing attention from many researchers with its various applications, such as enhancing traffic safety by collecting and disseminating traffic event information. This increased interest in VANET has necessitated greater scrutiny of machine learning (ML) methods used for improving the security capabilities of intrusion detection systems (IDSs), such as the need to solve computationally intensive ML problems due to the increased vehicular data. Therefore, in this paper, we propose a hybrid ML model to enhance the performance of IDSs by dealing with the explosive growth in computing power and the need for detecting malicious incidents timely. The proposed approach mainly uses the advantages of Random Forest to detect known network intrusions. Besides, there is a post-detection phase to detect possible novel intruders by using the advantages of coresets and clustering algorithms. Our approach is evaluated over a very recent IDS dataset named CICIDS2017. The preliminary results show that the proposed hybrid model can increase the utility of IDSs. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

This paper proposes a hybrid ML model to enhance the performance of IDSs, mainly using Random Forest to detect known network intrusions and utilizing coresets and clustering algorithms to detect possible novel intruders. Preliminary results show that the proposed hybrid model can increase the utility of IDSs.