IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT

Markus Miettinen, Samuel Marchal, Ibbad Hafeez, Ahmad-Reza Sadeghi, Nadarajah Asokan, Sasu Arimo Olavi Tarkoma

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IoT Sentinel, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IoT Sentinel is effective in identifying device types and has minimal performance overhead.
Originalspråkengelska
Titel på gästpublikation2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) : (ICDCS 2017)
RedaktörerKisung Lee, Ling Liu
Antal sidor8
FörlagIEEE
Utgivningsdatum5 jun 2017
Sidor2177-2184
ISBN (tryckt)978-1-5386-1791-5
DOI
StatusPublicerad - 5 jun 2017
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangInternational Conference on Distributed Computing Systems - Atlanta, Förenta Staterna (USA)
Varaktighet: 5 jun 20178 jun 2017
Konferensnummer: 37

Publikationsserier

NamnInternational Conference on Distributed Computing Systems
ISSN (tryckt)1063-6927

Bibliografisk information

ArXiv: http://arxiv.org/abs/1611.04880v2

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap

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