A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning.

ABSTRACT: Autonomous transportation systems have the potential to greatly impact the way we travel. A vital aspect of these systems is their connectivity, facilitated by intelligent transport applications. However, the safety ensured by the vehicular network can be easily compromised by malicious traffic with the exponential growth of IoT devices. One aspect is malicious traffic identification in Vehicular networks. We proposed a hybrid approach uses automated feature engineering via correlation-based feature selection (CFS) and principal component analysis (PCA)-based dimensionality reduction to reduce feature matrix size before a series of dense layers are used for classification. The intended use of CFS and PCA in the machine learning pipeline serves two folds benefit, first is that the resultant

SUBMITTER: Hassan F 

PROVIDER: S-EPMC11801628 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature