# CNN-based IoT Device Identification: A Comparative Study on Payload vs. Fingerprint

Source: [CNN-based IoT Device Identification: A Comparative Study on Payload vs. Fingerprint](<https://www.sota2.com/research/paper/68fa6f261e209131cea440d9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-29T05:00:05.134Z

Authors: Kahraman Kostas

Published: 2023-04-27T00:37:16.000Z

## Abstract

The proliferation of the Internet of Things (IoT) has introduced a massive influx of devices into the market, bringing with them significant security vulnerabilities. In this diverse ecosystem, robust IoT device identification is a critical preventive measure for network security and vulnerability management. This study proposes a deep learning-based method to identify IoT devices using the Aalto dataset. We employ Convolutional Neural Networks (CNN) to classify devices by converting network packet payloads into pseudo-images. Furthermore, we compare the performance of this payload-based approach against a feature-based fingerprinting method. Our results indicate that while the fingerprint-based method is significantly faster (approximately 10x), the payload-based image classification achieves comparable accuracy, highlighting the trade-offs between computational efficiency and data granularity in IoT security.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2304.13894>)

[PDF](<https://arxiv.org/pdf/2304.13894.pdf>)