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dc.contributor.authorPopescu, Dianaen
dc.contributor.authorSafronov, Vadimen
dc.contributor.authorYadav, Poonamen
dc.contributor.authorKolcun, Romanen
dc.contributor.authorMandalari, Anna Mariaen
dc.contributor.authorHaddadi, Hameden
dc.contributor.authorMcAuley, Dereken
dc.contributor.authorMortier, Richarden
dc.contributor.editorGanti, RKen
dc.contributor.editorJiang, XFen
dc.contributor.editorPicco, GPen
dc.contributor.editorZhou, Xen
dc.date.accessioned2020-01-21T13:20:19Z
dc.date.available2020-01-21T13:20:19Z
dc.date.issued2019en
dc.identifier.isbn978-1-4503-6950-3en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301114
dc.description.abstractAs 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
dc.publisherACM
dc.rightsAll rights reserved
dc.rights.uri
dc.title"Sensing" the IoT network: Ethical capture of domestic IoT network traffic: poster abstract.en
dc.typeConference Object
prism.endingPage407
prism.publicationDate2019en
prism.publicationNameSenSysen
prism.publicationNameProceedings of the 17th Conference on Embedded Networked Sensor Systems, SenSys 2019, New York, NY, USA, November 10-13, 2019.en
prism.startingPage406
dc.identifier.doi10.17863/CAM.48190
rioxxterms.versionAM*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019en
dc.contributor.orcidPopescu, Diana [0000-0002-2435-9603]
dc.contributor.orcidMortier, Richard [0000-0001-5205-5992]
rioxxterms.typeConference Paper/Proceeding/Abstracten
dc.identifier.urlhttps://dl.acm.org/citation.cfm?id=3356250en


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