# Communication Traffic Characteristics Reveal an IoT Devices Identity

arXiv: https://arxiv.org/abs/2402.16173 · HTML: https://mlsanity.com/paper/2402.16173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 25 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT](https://mlsanity.com/paper/1611.04880/) (2016)
- [Network Traffic Analysis based IoT Device Identification](https://mlsanity.com/paper/2009.04682/) (2020)
- [Item Recommendation Using User Feedback Data and Item Profile](https://mlsanity.com/paper/2206.14133/) (2022)
- [Device identification using optimized digital footprints](https://mlsanity.com/paper/2212.04354/) (2022)

## Abstract (arXiv)

Internet of Things (IoT) is one of the technological advancements of the twenty-first century which can improve living standards. However, it also imposes new types of security challenges, including device authentication, traffic types classification, and malicious traffic identification, in the network domain. Traditionally, internet protocol (IP) and media access control (MAC) addresses are utilized for identifying network-connected devices in a network, whilst these addressing schemes are prone to be compromised, including spoofing attacks and MAC randomization. Therefore, device identification using only explicit identifiers is a challenging task. Accurate device identification plays a key role in securing a network. In this paper, a supervised machine learning-based device fingerprinting (DFP) model has been proposed for identifying network-connected IoT devices using only communication traffic characteristics (or implicit identifiers). A single transmission control protocol/internet protocol (TCP/IP) packet header features have been utilized for generating unique fingerprints, with the fingerprints represented as a vector of 22 features. Experimental results have shown that the proposed DFP method achieves over 98% in classifying individual IoT devices using the UNSW dataset with 22 smart-home IoT devices. This signifies that the proposed approach is invaluable to network operators in making their networks more secure.