@inproceedings{998df4dd2d9f43ad8f6529a8da1c232c,
title = "Similarity-Based Clustering For IoT Device Classification",
abstract = "Classifying devices connected to an enterprise network is a fundamental security control that is nevertheless challenging due to the limitations of fingerprint-based classification and black-box machine learning. In this paper, we address such limitations by proposing a similarity-based clustering method. We evaluate our solution and compare it to a state-of-the-art fingerprint-based classification engine using data from 20,000 devices. The results show that we can successfully classify around half of the unclassified devices with a high accuracy. We also validate our approach with domain experts to demonstrate its usability in producing new fingerprinting rules.",
keywords = "Performance evaluation, Semantics, Process control, Machine learning, Manuals, Fingerprint recognition, Reliability engineering, Classification, Clustering, Internet of Things",
author = "Guillaume Dupont and Cristoffer Leite and \{dos Santos\}, \{Daniel Ricardo\} and Elisa Costante and Hartog, \{Jerry den\} and Sandro Etalle",
year = "2021",
month = sep,
day = "2",
doi = "10.1109/COINS51742.2021.9524201",
language = "English",
booktitle = "2021 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2021",
publisher = "Institute of Electrical and Electronics Engineers",
address = "United States",
note = "2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS) ; Conference date: 23-08-2021 Through 25-08-2021",
}