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With the number of connected smart devices expected to constantly grow in thenext years, Internet of Things (IoT) solutions are experimenting a boomingdemand to make data collection and processing easier. The ability of IoTappliances to provide pervasive and better support to everyday tasks, in mostcases transparently to humans, is also achieved through the high degree ofautonomy of such devices. However, the higher the number of new capabilitiesand services provided in an autonomous way, the wider the attack surface thatexposes users to data hacking and lost. In this scenario, many criticalchallenges arise also because IoT devices have heterogeneous computationalcapabilities (i.e., in the same network there might be simple sensors/actuatorsas well as more complex and smart nodes). In this paper, we try to provide acontribution in this setting, tackling the non-trivial issues of equippingsmart things with a strategy to evaluate, also through their neighbors, thetrustworthiness of an object in the network before interacting with it. To doso, we design a novel and fully distributed trust model exploiting devices'behavioral fingerprints, a distributed consensus mechanism and the Blockchaintechnology. Beyond the detailed description of our framework, we alsoillustrate the security model associated with it and the tests carried out toevaluate its correctness and performance.

A Novel IoT Trust Model Leveraging Fully Distributed Behavioral Fingerprinting and Secure Delegation