Real-time IoT Device Activity Detection in Edge Networks

Ibbad Hafeez, Yi Ding, Markku Antikainen, Sasu Tarkoma

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.
Original languageEnglish
Title of host publicationNetwork and System Security : 12th International Conference, NSS 2018, Hong Kong, China, August 27-29, 2018, Proceedings
EditorsMan Ho Au, Siu Ming Yiu, Jin Li, Xiapu Luo, Cong Wang, Aniello Castiglione, Kamil Kluczniak
Number of pages16
Place of PublicationCham
Publication date28 Aug 2018
ISBN (Print)978-3-030-02743-8
ISBN (Electronic)978-3-030-02744-5
Publication statusPublished - 28 Aug 2018
MoE publication typeA4 Article in conference proceedings
Event12th International Conference on Network and System Security - Hong Kong Polytechnic University, Hong Kong, China
Duration: 27 Aug 201829 Aug 2018
Conference number: 12

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fields of Science

  • 113 Computer and information sciences

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