# Device identification using optimized digital footprints

arXiv: https://arxiv.org/abs/2212.04354 · HTML: https://mlsanity.com/paper/2212.04354/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 4 Dec 2022.

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

- [Device Fingerprinting in Wireless Networks: Challenges and Opportunities](https://mlsanity.com/paper/1501.01367/) (2015)
- [IoTSense: Behavioral Fingerprinting of IoT Devices](https://mlsanity.com/paper/1804.03852/) (2018)
- [Network Traffic Analysis based IoT Device Identification](https://mlsanity.com/paper/2009.04682/) (2020)
- [IoT or NoT: Identifying IoT Devices in a ShortTime Scale](https://mlsanity.com/paper/1910.05647/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Hypergraph-Based Approach to Recommend Online Resources in a Library](https://mlsanity.com/paper/2312.01007/) (2023)
- [Communication Traffic Characteristics Reveal an IoT Devices Identity](https://mlsanity.com/paper/2402.16173/) (2024)

## Abstract (arXiv)

The rapidly increasing number of internet of things (IoT) and non-IoT devices has imposed new security challenges to network administrators. Accurate device identification in the increasingly complex network structures is necessary. In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network. A subset of nine features have been selected from the network and transport layers of a single transmission control protocol/internet protocol packet based on attribute evaluators in Weka, to generate device-specific signatures. The method has been evaluated on two online datasets, and an experimental dataset, using different supervised machine learning (ML) algorithms. Results have shown that the method is able to distinguish device type with up to 100% precision using the random forest (RF) classifier, and classify individual devices with up to 95.7% precision. These results demonstrate the applicability of the proposed DFP method for device identification, in order to provide a more secure and robust network.