Real-time IoT Device Activity Detection in Edge Networks

Ibbad Hafeez, Yi Ding, Markku Antikainen, Sasu Tarkoma

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu


The growing popularity of Internet-of-Things (IoT) has created the need for network-based traffic anomaly detection systems that could identify misbehaving devices. In this work, we propose a lightweight technique, IoTguard, for identifying malicious traffic flows. IoTguard uses semi-supervised learning to distinguish between malicious and benign device behaviours using the network traffic generated by devices. In order to achieve this, we extracted 39 features from network logs and discard any features containing redundant information. After feature selection, fuzzy C-Mean (FCM) algorithm was trained to obtain clusters discriminating benign traffic from malicious traffic. We studied the feature scores in these clusters and use this information to predict the type of new traffic flows. IoTguard was evaluated using a real-world testbed with more than 30 devices. The results show that IoTguard achieves high accuracy (≥ 98%), in differentiating various types of malicious and benign traffic, with low false positive rates. Furthermore, it has low resource footprint and can operate on OpenWRT enabled access points and COTS computing boards.
OtsikkoNetwork and System Security : 12th International Conference, NSS 2018, Hong Kong, China, August 27-29, 2018, Proceedings
ToimittajatMan Ho Au, Siu Ming Yiu, Jin Li, Xiapu Luo, Cong Wang, Aniello Castiglione, Kamil Kluczniak
Julkaisupäivä28 elok. 2018
ISBN (painettu)978-3-030-02743-8
ISBN (elektroninen)978-3-030-02744-5
DOI - pysyväislinkit
TilaJulkaistu - 28 elok. 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
Tapahtuma12th International Conference on Network and System Security - Hong Kong Polytechnic University, Hong Kong, Kiina
Kesto: 27 elok. 201829 elok. 2018
Konferenssinumero: 12


NimiLecture Notes in Computer Science
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349


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