POSTER: Intrusion Detection System for In-vehicle Networks using Sensor Correlation and Integration

The increasing utilization of Electronic Control Units (ECUs) and wireless connectivity in modern vehicles has favored the emergence of security issues. Recently, several attacks have been demonstrated against in-vehicle networks therefore drawing significant attention. This paper presents an Intrusion Detection System (IDS) based on a regression learning approach which estimates certain parameters by using correlated/redundant data. The estimated values are compared to observed ones to identify abnormal contexts that would indicate intrusion. Experiments performed with real-world vehicular data have shown that more than 90% of vehicle speed data can be precisely estimated within the error bound of 3 kph. The proposed IDS is capable of detecting and localizing attacks in real-time, which is fundamental to achieve automotive security.