CNN based IoT Device Identification

Overview

In this repository you will find a Python implementation of the methods in the paper CNN based IoT Device Identification.

Summary

While the use of the Internet of Things is becoming more and more popular, many security vulnerabilities are emerging with the large number of devices being introduced to the market. In this environment, IoT device identification methods provide a preventive security measure as an important factor in identifying these devices and detecting the vulnerabilities they suffer from. In this study, we present a method that identifies devices in the Aalto dataset/IoT devices captures using the convolutional neural network (CNN).

Requirements and Infrastructure:

Wireshark and Python 3.10 were used to create the application files. Before running the files, it must be ensured that Wireshark, Python 3.10+ and the following libraries are installed.

Library	Task
Scapy	Packet(Pcap) crafting
tshark	Packet(Pcap) crafting
Sklearn	Machine Learning & Data Preparation
Numpy	Mathematical Operations
Pandas	Data Analysis
Scipy	Data Analysis, Mathematical Operations
Matplotlib	Graphics and Visuality
Seaborn	Graphics and Visuality
Keras	Deep Learning

The technical specifications of the computer used for experiments are given below.


Central Processing Unit	:	Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz 2.90 GHz
Random Access Memory	:	8 GB (7.74 GB usable)
Operating System	:	Windows 10 Pro 64-bit
Graphics Processing Unit	:	AMD Readon (TM) 530

Data:

Full Datasets

The processed datasets are shared in depository. However, raw versions of the datasets used in the study and their addresses are given below.

Dataset	capture year	Number of Devices	Type
Aalto University	2016	31	Benign

License

This project is licensed under the MIT License - see the LICENSE file for details

Citations

If you use the source code please cite the following paper:

@misc{kostas2023CNN,
      title={{CNN} based {IoT} Device Identification]}, 
      author={Kahraman Kostas},
      year={2023},
      eprint={2304.13905},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}

Contact: Kahraman Kostas [email protected]

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