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

TL;DR

This paper tackles trust management in large, heterogeneous IoT networks by proposing a fully distributed framework that uses behavioral fingerprinting, privacy-preserving secure delegation, and a blockchain-backed consensus to assess object reliability before interactions. It introduces a lightweight GRU-based fingerprint model suitable for constrained devices, a community-driven delegation mechanism via IPFS, and a consensus process that aggregates multiple evaluators while guarding against attacks through hash-chained nonces and on-chain reliability tracking. Empirical results show near-parity with heavier baselines (≈1% difference in accuracy) and substantial reductions in model size, with practical execution times across device tiers and effective anomaly detection via consensus. The approach promises improved interaction safety, privacy, and resilience in IoT, and points to future work on group behavior fingerprints and distributed attack detection.

Abstract

With the number of connected smart devices expected to constantly grow in the next years, Internet of Things (IoT) solutions are experimenting a booming demand to make data collection and processing easier. The ability of IoT appliances to provide pervasive and better support to everyday tasks, in most cases transparently to humans, is also achieved through the high degree of autonomy of such devices. However, the higher the number of new capabilities and services provided in an autonomous way, the wider the attack surface that exposes users to data hacking and lost. In this scenario, many critical challenges arise also because IoT devices have heterogeneous computational capabilities (i.e., in the same network there might be simple sensors/actuators as well as more complex and smart nodes). In this paper, we try to provide a contribution in this setting, tackling the non-trivial issues of equipping smart things with a strategy to evaluate, also through their neighbors, the trustworthiness of an object in the network before interacting with it. To do so, we design a novel and fully distributed trust model exploiting devices' behavioral fingerprints, a distributed consensus mechanism and the Blockchain technology. Beyond the detailed description of our framework, we also illustrate the security model associated with it and the tests carried out to evaluate its correctness and performance.

Figures (6)

• Figure 1: The general architecture of our solution
• Figure 2: An example of e_paths identification in our scenario
• Figure 3: An example of our secure delegation strategy
• Figure 4: Traffic analysis with windows of different sizes: a) 25 packets size and b) 400 packets size.
• Figure 5: Number of Packets required to detect an anomaly.