International audience—Embedded electronic components, so-called ECU (Electronic Controls Units), are nowadays a prominent part of a car's architecture. These ECUs, monitoring and controlling the different subsystems of a car, are interconnected through several gateways and compose the global internal network of the car. Moreover, modern cars are now able to communicate with other devices through wired or wireless interfaces such as USB, Bluetooth, WiFi or even 3G. Such interfaces may expose the internal network to the outside world and can be seen as entry points for cyber attacks. In this paper, we present a survey on security threats and protection mechanisms in embedded automotive networks. After introducing the different protocols being used in the embedded networks of current vehicles, we then analyze the potential threats targeting these networks and describe how the attackers' opportunities can be enhanced by the new communication abilities of modern cars. Finally, we present the security solutions currently being devised to address these problems

The increase in connectivity and complexity of modern automotive networks presents new opportunities for potential hackers trying to take over a vehicle. To protect the automotive networks from such attacks, security mechanisms, such as firewalls or secure authentication protocols may be included. However, should an attacker succeed in bypassing such measures and gain access to the internal network, these security mechanisms become unable to report about the attacks ensuing such a breach, occurring from the internal network. To complement these preventive security mechanisms, we present a non intrusive network-based intrusion detection approach fit for vehicular networks, such as the widely used CAN. Leveraging the high predictability of embedded automotive systems, we use language theory to elaborate a set of attack signatures derived from behavioural models of the automotive calculators in order to detect a malicious sequence of messages transiting through the internal network.