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

Kahraman Kostas
4/27/2023
cs.CRcs.AI

No official code found. Best implementation: kahramankostas/CNN-based-IoT-Device-Identification (10 stars, last commit 2023-04-28).

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.

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Code Implementations(1)

kahramankostas/CNN-based-IoT-Device-IdentificationCommunity100%

Multi-class classification via CNN using fingerprints extracted from IoT devices captures data.

102Jupyter Notebook3 years ago
cnndeep-learningdevice-identificationfingerprintingiot+2 more

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Cite this paper

@article{kostas2023cnnbased,
  title  = {CNN-based IoT Device Identification: A Comparative Study on Payload vs. Fingerprint},
  author = {Kahraman Kostas},
  year   = {2023},
  eprint = {2304.13894},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.13894v2}
}

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