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A Quantitative Analysis of the Insecurity of Embedded Network Devices: Results of a Wide-Area Scan

Cui, Ang; Stolfo, Salvatore

We present a quantitative lower bound on the number of vulnerable embedded device on a global scale. Over the past year, we have systematically scanned large portions of the internet to monitor the presence of trivially vulnerable embedded devices. At the time of writing, we have identified over 540,000 publicly accessible embedded devices configured with factory default root passwords. This constitutes over 13% of all discovered embedded devices. These devices range from enterprise equipment such as firewalls and routers to consumer appliances such as VoIP adapters, cable and IPTV boxes to office equipment such as network printers and video conferencing units. Vulnerable devices were detected in 144 countries, across 17,427 unique private enterprise, ISP, government, educational, satellite provider as well as residential network environments. Preliminary results from our longitudinal study tracking over 102,000 vulnerable devices revealed that over 96% of such accessible devices remain vulnerable after a 4-month period. We believe the data presented in this paper provides a conservative lower bound on the actual population of vulnerable devices in the wild. By combining the observed vulnerability distributions and its potential root causes, we propose a set of mitigation strategies and hypothesize about its quantitative impact on reducing the global vulnerable embedded device population. Employing our strategy, we have partnered with Team Cymru to engage key organizations capable of significantly reducing the number of trivially vulnerable embedded devices currently on the internet. As an ongoing longitudinal study, we plan to gather data continuously over the next year in order to quantify the effectiveness of community's cumulative effort to mitigate this pervasive threat.

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Published In
26th Annual Computer Security Applications Conference: Proceedings: Austin, Texas, USA: 6-10 December, 2010
Publisher DOI
https://doi.org/10.1145/1920261.1920276
Pages
97 - 106
Publisher
Association for Computing Machinery
Publication Origin
New York
Academic Units
Computer Science
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