Machine Learning-based Intrusion Detection Systems (IDS) in Connected Vehicles

Project Summary: The recent development in wireless technologies such as LTE+ and 5G has fueled the exponential growth in the IoT-centric connectivity across the smart city/systems platforms. One of such growing application domain is connected vehicles. Cyber attacks pose a significant threat to the connected vehicle ecosystem, and this domain is a major target of intruders. This study gives a detailed view of the connected vehicle attack types and their defense mechanisms. This work investigated the efficiency and the accuracy of various machine learning-based anomaly detection models and proposed an effective network intrusion detection model to mitigate the cyber attacks in the connected vehicle platform.