Intrusion detection in vehicular networks using machine learning - Ventura, Heitor Tonel; Moretti, Raian de Almeida; Vendramin, Ana Cristina Barreiras Kochem; Pigatto, Daniel Fernando (Journal of Internet Services and Applications, 2025)
Vehicular networks and intelligent transport systems play a critical role in modern urban mobility. In order to improve urban transportation in smart cities, vehicles and fixed stations exchange information about traffic, road conditions, and accidents, allowing better decision-making and ensuring greater safety for the population. However, to provide security, a vehicular network must be resilient to attacks. Anomaly detection models are a potential solution to the reduced effectiveness of signature-based intrusion detection systems, which struggle to detect new attacks due to the absence of previous signatures. Leveraging artificial intelligence in intrusion detection systems becomes relevant, as it allows learning from a vast amount of data. However, many models proposed for anomaly detection based on machine learning lack validation and application in vehicular networks, thus lacking evidence of promising results in these specific contexts. Therefore, this work aims to address this gap by comparing two models used in anomaly detection in the context of vehicular networks: the CNN-LSTM model that has already been applied in the area of vehicular networks and the TranAD model that needed to be adapted for this type of network. The results demonstrate that the CNN-LSTM model provides superior performance, presenting an F1 of 0.9585 against 0.8839 of TranAD in the scenario in which both models obtained the best result.