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.
Originalspråk | engelska |
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Titel på värdpublikation | Network and System Security : 12th International Conference, NSS 2018, Hong Kong, China, August 27-29, 2018, Proceedings |
Redaktörer | Man Ho Au, Siu Ming Yiu, Jin Li, Xiapu Luo, Cong Wang, Aniello Castiglione, Kamil Kluczniak |
Antal sidor | 16 |
Utgivningsort | Cham |
Förlag | Springer Nature |
Utgivningsdatum | 28 aug. 2018 |
Sidor | 221-236 |
ISBN (tryckt) | 978-3-030-02743-8 |
ISBN (elektroniskt) | 978-3-030-02744-5 |
DOI | |
Status | Publicerad - 28 aug. 2018 |
MoE-publikationstyp | A4 Artikel i en konferenspublikation |
Evenemang | 12th International Conference on Network and System Security - Hong Kong Polytechnic University, Hong Kong, Kina Varaktighet: 27 aug. 2018 → 29 aug. 2018 Konferensnummer: 12 http://www4.comp.polyu.edu.hk/~nss2018/program.html |
Publikationsserier
Namn | Lecture Notes in Computer Science |
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Volym | 11058 |
ISSN (tryckt) | 0302-9743 |
ISSN (elektroniskt) | 1611-3349 |
Vetenskapsgrenar
- 113 Data- och informationsvetenskap