Development of FPGA based intrusion detection system for CAN bus

Today's vehicle systems have been developing rapidly. The number of electrical units in the vehicle systems are increasing. With the opening of the in-vehicle electronic units to the outside world with different communication networks, the security of the data communication of these units has been the subject of research. In vehicle systems CAN bus is used to communicate between electrical control units (ECU). CAN bus is a very reliable communication network according to bit error rate. However, CAN bus has numerous vulnerabilities in terms of security. In order to seal these security vulnerabilities many intrusion detection systems (IDS) have been developed. With intrusion detection systems, it is aimed to detect attacks on the CAN bus and take countermeasures. In this thesis, an intrusion detection system which can detect attacks in the physical layer of the CAN bus. For his purpose, CAN signals, which are produced by ECUs, were analyzed. It is proven that ECU footprints can be extracted from CAN signals. In this thesis, to extract footprint of signals, a CAN IP core was implemented on FPGA. Extracted footprints were used to train different neural network architectures which are MLP and LSTM. Trained neural networks were used to detect intrusions by using signal footprints of intruder ECUs.