Abstract
In-vehicle buses and the Controller Area Network (CAN) in particular have been shown to be vulnerable to adversarial actions. We embed adversary models and intrusion detection systems (IDS) inside a CANoe based application. Based on real-world CAN traces collected from several vehicles we build attack traces that are subject to intrusion detection algorithms. We also take benefit from existing machine-learning support in MATLAB that is ported via C++ code in CANoe in order to integrate intrusion detection functionality. A unified framework for attacks and intrusion detection has the benefit of providing a testbed for various intrusion detection algorithms. CANoe integration makes the use of these functionalities ready for realistic testing as CANoe is an industry-standard tool in the automotive domain.
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