Real-time IoT Device Activity Detection in Edge Networks

Ibbad Hafeez, Yi Ding, Markku Antikainen, Sasu Tarkoma

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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.
Originalspråkengelska
Titel på gästpublikationNetwork and System Security : 12th International Conference, NSS 2018, Hong Kong, China, August 27-29, 2018, Proceedings
RedaktörerMan Ho Au, Siu Ming Yiu, Jin Li, Xiapu Luo, Cong Wang, Aniello Castiglione, Kamil Kluczniak
Antal sidor16
UtgivningsortCham
FörlagSpringer Nature
Utgivningsdatum28 aug 2018
Sidor221-236
ISBN (tryckt)978-3-030-02743-8
ISBN (elektroniskt)978-3-030-02744-5
DOI
StatusPublicerad - 28 aug 2018
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemang12th International Conference on Network and System Security - Hong Kong Polytechnic University, Hong Kong, Kina
Varaktighet: 27 aug 201829 aug 2018
Konferensnummer: 12
http://www4.comp.polyu.edu.hk/~nss2018/program.html

Publikationsserier

NamnLecture Notes in Computer Science
Volym11058
ISSN (tryckt)0302-9743
ISSN (elektroniskt)1611-3349

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  • 113 Data- och informationsvetenskap

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