A Novel IoT Trust Model Leveraging Fully Distributed Behavioral Fingerprinting and Secure Delegation
Marco Arazzi, Serena Nicolazzo, Antonino Nocera

TL;DR
This paper introduces a fully distributed IoT trust model that uses behavioral fingerprinting, consensus, and blockchain to evaluate device trustworthiness, enhancing security in heterogeneous IoT networks.
Contribution
The paper presents a novel IoT trust model combining behavioral fingerprinting, distributed consensus, and blockchain technology for improved security.
Findings
The trust model effectively detects untrustworthy devices.
The framework demonstrates high performance and correctness in tests.
Enhanced security against data hacking and device impersonation.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBlockchain Technology Applications and Security · User Authentication and Security Systems · Adversarial Robustness in Machine Learning
