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

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

Researchers

Research units

  • Technische Universität Darmstadt
  • University of Helsinki

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.

Details

Original languageEnglish
Title of host publication2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)
EditorsKisung Lee, Ling Liu
Publication statusPublished - 1 Jun 2017
MoE publication typeA4 Article in a conference publication
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. Proceedings
PublisherIEEE Computer Society
Volume37
ISSN (Print)1063-6927

Conference

ConferenceInternational Conference on Distributed Computing Systems
Abbreviated titleICDCS
CountryUnited States
CityAtlanta
Period05/06/201708/06/2017

    Research areas

  • Internet of Things, security of data, Internet-of-Things, IoT SENTINEL, IoT network, automated device-type identification, security enforcement, IP networks, Logic gates, Object recognition, Ports (Computers), Protocols, Security, Wireless fidelity, IoT security, device fingerprinting, device identification, threat mitigation

ID: 15241411