Detecting Cyber Attacks In-Vehicle Diagnostics Using an Intelligent Multistage Framework

The advanced technology of vehicles makes them vulnerable to external exploitation. The current trend of research is to impose security measures to protect vehicles from different aspects. One of the main problems that counter Intrusion Detection Systems (IDSs) is the necessity to have a low false acceptance rate (FA) with high detection accuracy without major changes in the vehicle network infrastructure. Furthermore, the location of IDSs can be controversial due to the limitations and concerns of Electronic Control Units (ECUs). Thus, we propose a novel framework of multistage to detect abnormality in vehicle diagnostic data based on specifications of diagnostics and stacking ensemble for various machine learning models. The proposed framework is verified against the KIA SOUL and Seat Leon 2018 datasets. Our IDS is evaluated against point anomaly attacks and period anomaly attacks that have not been used in its training. The results show the superiority of the framework and its robustness with high accuracy of 99.21%, a low false acceptance rate of 0.003%, and a good detection rate (DR) of 99.63% for Seat Leon 2018, and an accuracy of 99.22%, a low false acceptance rate of 0.005%, and good detection rate of 98.59% for KIA SOUL.