LFD-IDS: bagging-based data poisoning attacks against cyberattack detection in connected vehicle

This paper explores the need for new systems to detect and monitor cyberattacks in Connected Vehicles (CVs). Sensor health in CVs is vital, as prediction errors and communication issues can weaken the sensor network. Intrusion Detection Systems (IDS) for CVs must be continuously updated to meet changing needs and be robust against adversarial attacks. We developed a new Label Flipping system against Deep learning-based IDS (LFD-IDS) to help cloud operators understand unusual vehicle sensor data. LFD-IDS specifically targets detecting and explaining sensor data manipulation from poisoning attacks. We proposed two label-flipping attacks based on Bootstrapping and Bagging and a defensive strategy using a multi-layer deep neural network. Our LFD-IDS achieves at least 90% accuracy in identifying cyberattacks.