NDNIDS: An Intrusion Detection System for NDN Based VANET

Vehicular Ad-hoc NETworks (VANETs) enable communication between vehicles to ensure road safety. In VANET communication, three types of messages exist, viz., safety, transport efficiency, and infotainment. Safety messages are crucial and if being falsified by an intruder endangers the lives of the drivers and passengers. The existing methods that detect the intrusions in conventional networks fail in VANET due to high mobility, intermittent connections, link disruptions, etc. A legitimate vehicle with malicious intent could falsely broadcast a traffic warning message. Therefore, to verify the integrity of the message, an adaptive Intrusion Detection System (IDS) is proposed in this paper. In IDS, a heuristic detection process is used. The set of efficient rules are created using the multiple sensor values present in the On-Board Unit (OBU) of the vehicle and an appropriate threshold value is identified for each sensor. Besides, driver's heart beat rate value is also used for the message discrimination. To the best of our knowledge, our paper is the first to propose an adaptive and less complex rule based IDS for NDN based VANET. We have developed a NDN simulator (available in github) in Python to test the proposed IDS. From the experimental results, it is evident that our proposed IDS differentiate the fake messages from the genuine messages under various test cases.