Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning

Tony Hämmäinen; Julen Kahles

doi:10.1109/WiMob52687.2021.9606307

Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning

Tony Hämmäinen
, Julen Kahles

Tuotantotalouden laitos

Ericsson Finland

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

1 Sitaatiot (Scopus)

182 Lataukset (Pure)

Abstrakti

We propose a novel machine-learning pipeline for clustering unknown IoT devices in an industrial 5G mobile-network setting. Organizing IoT devices as few homogeneous device groups improves the applicability of network-intrusion detection systems. More specifically, we develop feature engineering methods that transform IP-flows into device-level data points, define distance metrics between the data points, and apply the DBSCAN algorithm on them. Our experiments on a simulated IoT device network with varying levels of noise show that our proposed methodology outperforms alternative methods and is the only one producing a robust grouping of the IoT devices with noise present in the traffic data.

Alkuperäiskieli	Englanti
Otsikko	2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)
Kustantaja	IEEE
Sivumäärä	6
ISBN (elektroninen)	978-1-6654-2854-5
DOI - pysyväislinkit	https://doi.org/10.1109/WiMob52687.2021.9606307
Tila	Julkaistu - 22 marrask. 2021
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Conference on Wireless and Mobile Computing, Networking and Communications - Bologna, Italia Kesto: 11 lokak. 2021 → 13 lokak. 2021 Konferenssinumero: 17

Julkaisusarja

Nimi	IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications
Kustantaja	IEEE
ISSN (elektroninen)	2160-4894

Workshop

Workshop	IEEE International Conference on Wireless and Mobile Computing, Networking and Communications
Lyhennettä	WiMob
Maa/Alue	Italia
Kaupunki	Bologna
Ajanjakso	11/10/2021 → 13/10/2021

Pääsy asiakirjaan

10.1109/WiMob52687.2021.9606307

SCI_Hammainen_Kahles_Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine LearningAccepted author manuscript, 542 KB

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Hämmäinen, T., & Kahles, J. (2021). Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning. teoksessa 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications). IEEE. https://doi.org/10.1109/WiMob52687.2021.9606307

@inproceedings{a12908c63bc84795b00177338fef6d89,

title = "Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning",

abstract = "We propose a novel machine-learning pipeline for clustering unknown IoT devices in an industrial 5G mobile-network setting. Organizing IoT devices as few homogeneous device groups improves the applicability of network-intrusion detection systems. More specifically, we develop feature engineering methods that transform IP-flows into device-level data points, define distance metrics between the data points, and apply the DBSCAN algorithm on them. Our experiments on a simulated IoT device network with varying levels of noise show that our proposed methodology outperforms alternative methods and is the only one producing a robust grouping of the IoT devices with noise present in the traffic data.",

author = "Tony H{\"a}mm{\"a}inen and Julen Kahles",

year = "2021",

month = nov,

day = "22",

doi = "10.1109/WiMob52687.2021.9606307",

language = "English",

series = "IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications",

publisher = "IEEE",

booktitle = "2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)",

address = "United States",

note = "IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob ; Conference date: 11-10-2021 Through 13-10-2021",

}

Hämmäinen, T & Kahles, J 2021, Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning. julkaisussa 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications, IEEE, IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, Bologna, Italia, 11/10/2021. https://doi.org/10.1109/WiMob52687.2021.9606307

Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning. / Hämmäinen, Tony; Kahles, Julen.
2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, 2021. (IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

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T1 - Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning

AU - Hämmäinen, Tony

AU - Kahles, Julen

N1 - Conference code: 17

PY - 2021/11/22

Y1 - 2021/11/22

N2 - We propose a novel machine-learning pipeline for clustering unknown IoT devices in an industrial 5G mobile-network setting. Organizing IoT devices as few homogeneous device groups improves the applicability of network-intrusion detection systems. More specifically, we develop feature engineering methods that transform IP-flows into device-level data points, define distance metrics between the data points, and apply the DBSCAN algorithm on them. Our experiments on a simulated IoT device network with varying levels of noise show that our proposed methodology outperforms alternative methods and is the only one producing a robust grouping of the IoT devices with noise present in the traffic data.

AB - We propose a novel machine-learning pipeline for clustering unknown IoT devices in an industrial 5G mobile-network setting. Organizing IoT devices as few homogeneous device groups improves the applicability of network-intrusion detection systems. More specifically, we develop feature engineering methods that transform IP-flows into device-level data points, define distance metrics between the data points, and apply the DBSCAN algorithm on them. Our experiments on a simulated IoT device network with varying levels of noise show that our proposed methodology outperforms alternative methods and is the only one producing a robust grouping of the IoT devices with noise present in the traffic data.

UR - https://www.scopus.com/pages/publications/85123009612

U2 - 10.1109/WiMob52687.2021.9606307

DO - 10.1109/WiMob52687.2021.9606307

M3 - Conference article in proceedings

T3 - IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications

BT - 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

PB - IEEE

T2 - IEEE International Conference on Wireless and Mobile Computing, Networking and Communications

Y2 - 11 October 2021 through 13 October 2021

ER -

Hämmäinen T, Kahles J. Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning. julkaisussa 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE. 2021. (IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications). doi: 10.1109/WiMob52687.2021.9606307

Clustering Unknown IoT Devices in a 5G Mobile Network Security Context via Machine Learning

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Julkaisusarja

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