"Sensing" the IoT network: Ethical capture of domestic IoT network traffic: poster abstract.
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Authors
Safronov, Vadim
Yadav, Poonam
Kolcun, Roman
Mandalari, Anna Maria
Haddadi, Hamed
McAuley, Derek
Editors
Ganti, RK
Jiang, XF
Picco, GP
Zhou, X
Publication Date
2019Journal Title
SenSys
Proceedings of the 17th Conference on Embedded Networked Sensor Systems, SenSys 2019, New York, NY, USA, November 10-13, 2019.
ISBN
978-1-4503-6950-3
Publisher
ACM
Pages
406-407
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
Popescu, D., Safronov, V., Yadav, P., Kolcun, R., Mandalari, A. M., Haddadi, H., McAuley, D., & et al. (2019). "Sensing" the IoT network: Ethical capture of domestic IoT network traffic: poster abstract.. SenSys, 406-407. https://dl.acm.org/citation.cfm?id=3356250
Abstract
As more and more devices are connected to the Internet-of-Things,
often made by non-specialist companies or short-lived startups,
the likelihood that these devices will be hacked and used for nefarious activity online increases. We seek to support non-expert users
in managing the network behaviour of their IoT devices, and assisting them in handling the cases where those devices are hacked. To
do so, we wish to enable anomaly detection at the network level, determining when a device starts behaving unusually. This requires
capturing data about how devices behave in a diverse range of real
deployments, not just lab environments.
To that end, we present IoTCrowdsourcery, a toolset for capturing traffic data from real-world IoT deployments. Participants collect packet traces from their IoT devices through our software, and
provide them via a crowdsourcing infrastructure. The key challenges to overcome are to make the process straightforward enough
for non-expert participants to carry out, and to ensure that legal
(notably GDPR) and ethical issues are carefully handled by ensuring that participants understand what they are doing, and are provided with various means to exercise agency in participating, and
ultimately to withdraw their participation if they wish. We envisage the captured traces being analysed to develop behavioural
models of IoT devices which will be used for anomaly detection,
improving the security of our smart homes and more generally of
the Internet
Identifiers
External link: https://dl.acm.org/citation.cfm?id=3356250
This record's URL: https://www.repository.cam.ac.uk/handle/1810/301114
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