  Increasing communication and self-driving capabilities for road vehicles lead
to threats imposed by attackers. Especially attacks leading to safety
violations have to be identified to address them by appropriate measures. The
impact of an attack depends on the threat exploited, potential countermeasures
and the traffic situation. In order to identify such attacks and to use them
for testing, we propose the systematic approach SaSeVAL for deriving attacks of
autonomous vehicles. SaSeVAL is based on threats identification and
safety-security analysis. The impact of automotive use cases to attacks is
considered. The threat identification considers the attack interface of
vehicles and classifies threat scenarios according to threat types, which are
then mapped to attack types. The safety-security analysis identifies the
necessary requirements which have to be tested based on the architecture of the
system under test. lt determines which safety impact a security violation may
have, and in which traffic situations the highest impact is expected. Finally,
the results of threat identification and safety-security analysis are used to
describe attacks. The goal of SaSeVAL is to achieve safety validation of the
vehicle w.r.t. security concerns. lt traces safety goals to threats and to
attacks explicitly. Hence, the coverage of safety concerns by security testing
is assured. Two use cases of vehicle communication and autonomous driving are
investigated to prove the applicability of the approach.
