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

arXiv: https://arxiv.org/abs/2310.00953 · HTML: https://mlsanity.com/paper/2310.00953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 79 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT](https://mlsanity.com/paper/1611.04880/) (2016)
- [IoT Device Fingerprint using Deep Learning](https://mlsanity.com/paper/1902.01926/) (2019)
- [IoT DoS and DDoS Attack Detection using ResNet](https://mlsanity.com/paper/2012.01971/) (2020)
- [The Internet of Things: New Interoperability, Management and Security Challenges](https://mlsanity.com/paper/1604.04824/) (2016)
- [A Trust Architecture for Blockchain in IoT](https://mlsanity.com/paper/1906.11461/) (2019)
- [Towards a secure behavior modeling for IoT networks using Blockchain](https://mlsanity.com/paper/2001.01841/) (2020)
- [IOTA Tangle 2.0: Toward a Scalable, Decentralized, Smart, and Autonomous IoT Ecosystem](https://mlsanity.com/paper/2209.04959/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT](https://mlsanity.com/paper/2404.03276/) (2024)

## Abstract (arXiv)

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.