Summary of hyperparameters.

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 feature matrix contains attributes that are most useful for recognizing malicious traffic, and second that after CFS and PCA, the feature matrix has a smaller dimensionality which in turn means that smaller number of weights need to be trained for the dense layers (connections are required for the dense layers) which resulting in smaller model size. Furthermore, we show the impact of post-training model weight quantization to further reduce the model size. Results demonstrate the effectiveness of feature engineering which improves the classification f1score from 96.48% to 98.43%. It also reduces the model size from 28.09 KB to 20.34 KB thus optimizing the model in terms of both classification performance and model size. Post-training quantization further optimizes the model size to 9 KB. The experimental results using CICIDS2017 dataset demonstrate that proposed hybrid model performs well not only in terms of classification performance but also yields trained models that have a low parameter count and model size. Thus, the proposed low-complexity models can be used for intrusion detection in VANET scenario.