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

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

Abstract

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.
Original languageEnglish
Title of host publication2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) : (ICDCS 2017)
EditorsKisung Lee, Ling Liu
Number of pages8
PublisherIEEE
Publication date5 Jun 2017
Pages2177-2184
ISBN (Print)978-1-5386-1791-5
DOIs
Publication statusPublished - 5 Jun 2017
MoE publication typeA4 Article in conference proceedings
EventInternational Conference on Distributed Computing Systems - Atlanta, United States
Duration: 5 Jun 20178 Jun 2017
Conference number: 37

Publication series

NameInternational Conference on Distributed Computing Systems
ISSN (Print)1063-6927

Bibliographical note

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

Fields of Science

  • 113 Computer and information sciences

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