  Autonomous collaborative networks of devices are rapidly emerging in numerous
domains, such as self-driving cars, smart factories, critical infrastructure,
and Internet of Things in general. Although autonomy and self-organization are
highly desired properties, they increase vulnerability to attacks. Hence,
autonomous networks need dependable mechanisms to detect malicious devices in
order to prevent compromise of the entire network. However, current mechanisms
to detect malicious devices either require a trusted central entity or scale
poorly.
  In this paper, we present GrandDetAuto, the first scheme to identify
malicious devices efficiently within large autonomous networks of collaborating
entities. GrandDetAuto functions without relying on a central trusted entity,
works reliably for very large networks of devices, and is adaptable to a wide
range of application scenarios thanks to interchangeable components. Our scheme
uses random elections to embed integrity validation schemes in distributed
consensus, providing a solution supporting tens of thousands of devices. We
implemented and evaluated a concrete instance of GrandDetAuto on a network of
embedded devices and conducted large-scale network simulations with up to
100000 nodes. Our results show the effectiveness and efficiency of our scheme,
revealing logarithmic growth in run-time and message complexity with increasing
network size. Moreover, we provide an extensive evaluation of key parameters
showing that GrandDetAuto is applicable to many scenarios with diverse
requirements.
