Efficient IoT Device Fingerprinting Approach using Machine Learning

Richmond Osei, Habib Louafi, Malek Mouhoub, Zhongwen Zhu

2022

Abstract

Internet of Things (IoT) usage is steadily becoming a way of life. IoT devices can be found in smart homes, factories, farming, etc. However, skyrocketing of IoT devices comes along with many security concerns due to their small and constrained build-up. For instance, a comprised IoT device in a network presents a vulnerability that can be exploited to attack the entire network. Since IoT devices are usually scattered over vast areas, Mobile Network Operators resort to analyzing the traffic generated by these devices to detect the identity (fingerprint) and nature of these devices (legitimate, faulty, or malicious). We propose an efficient solution to fingerprint IoT devices using known classifiers, alongside dimensionality reduction techniques, such as PCA and Autoencoder. The latter techniques extract the most relevant features required for accurate fingerprinting while reducing the amount of IoT data to process. To assess the performance of our proposed approach, we conducted several experiments on a real-world dataset from an IoT network. The results show that the Autoencoder for dimensionality reduction with a Decision Tree Algorithm reduces the number of features from 14 to 5 while keeping the prediction of the IoT devices fingerprints very high (97%).

Paper Citation

in Harvard Style

Osei R., Louafi H., Mouhoub M. and Zhu Z. (2022). Efficient IoT Device Fingerprinting Approach using Machine Learning. In Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT, ISBN 978-989-758-590-6, pages 525-533. DOI: 10.5220/0011260500003283

in Bibtex Style

@conference{secrypt22,
author={Richmond Osei and Habib Louafi and Malek Mouhoub and Zhongwen Zhu},
title={Efficient IoT Device Fingerprinting Approach using Machine Learning},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,},
year={2022},
pages={525-533},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011260500003283},
isbn={978-989-758-590-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,
TI - Efficient IoT Device Fingerprinting Approach using Machine Learning
SN - 978-989-758-590-6
AU - Osei R.
AU - Louafi H.
AU - Mouhoub M.
AU - Zhu Z.
PY - 2022
SP - 525
EP - 533
DO - 10.5220/0011260500003283