Agarwal, Sharad;
Oser, Pascal;
Lueders, Stefan;
(2019)
Detecting IoT Devices and How They Put Large Heterogeneous Networks at Security Risk.
Sensors
, 19
(19)
, Article 4107. 10.3390/s19194107.
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Abstract
The introduction of the Internet of Things (IoT), i.e., the interconnection of embedded devices over the Internet, has changed the world we live in from the way we measure, make calls, print information and even the way we get energy in our offices or homes. The convenience of IoT products, like closed circuit television (CCTV) cameras, internet protocol (IP) phones, and oscilloscopes, is overwhelming for end users. In parallel, however, security issues have emerged and it is essential for infrastructure providers to assess the associated security risks. In this paper, we propose a novel method to detect IoT devices and identify the manufacturer, device model, and the firmware version currently running on the device using the page source from the web user interface. We performed automatic scans of the large-scale network at the European Organization for Nuclear Research (CERN) to evaluate our approach. Our tools identified 233 IoT devices that fell into eleven distinct device categories and included 49 device models manufactured by 26 vendors from across the world.
Type: | Article |
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Title: | Detecting IoT Devices and How They Put Large Heterogeneous Networks at Security Risk |
Location: | Switzerland |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.3390/s19194107 |
Publisher version: | https://doi.org/10.3390/s19194107 |
Language: | English |
Additional information: | © The Author(s), 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
Keywords: | Internet of Things; security; vulnerabilities and protective measures; control network security; operation in multi-user environments; risk assessment |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10177789 |
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