Context Sensitive Access Control in Smart Home Environments

dc.contributor.authorDutta, Sofia
dc.contributor.authorChukkapalli, Sai Sree Laya
dc.contributor.authorSulgekar, Madhura
dc.contributor.authorKrithivasan, Swathi
dc.contributor.authorDas, Prajit Kumar
dc.contributor.authorJoshi, Anupam
dc.date.accessioned2020-07-22T15:49:44Z
dc.date.available2020-07-22T15:49:44Z
dc.date.issued2020-05-27
dc.description2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 25-27 May 2020, Baltimore, MD, USA, USAen_US
dc.description.abstractThe rise in popularity of Internet of Things (IoT) devices has opened doors for privacy and security breaches in Cyber-Physical systems like smart homes, smart vehicles, and smart grids that affect our daily existence. IoT systems are also a source of big data that gets shared via cloud. IoT systems in a smart home environment have sensitive access control issues since they are deployed in a personal space. The collected data can also be of highly personal nature. Therefore, it is critical to build access control models that govern who, under what circumstances, can access which sensed data or actuate a physical system. Traditional access control mechanisms are not expressive enough to handle such complex access control needs, warranting the incorporation of new methodologies for privacy and security. In this paper, we propose the creation of the PALS system, that builds upon existing work in attribute based access control model, captures physical context collected from sensed data (attributes), and performs dynamic reasoning over these attributes and context driven policies using Semantic Web technologies to execute access control decisions. Reasoning over user context, details of information collected by cloud service provider and device type our mechanism generates as a consequent access control decisions. Our system’s access control decisions are supplemented by another sub-system that detects intrusions into smart home systems based on both network and behavioral data. The combined approach serves to determine indicators that a smart home system is under attack, as well as limit what data breach such attacks can achieve.en_US
dc.description.sponsorshipThis research was partially supported by a grant from NIST and the Maryland Industrial Partnerships.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9123025en_US
dc.format.extent7 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2syhs-bwah
dc.identifier.citationS. Dutta, S. S. L. Chukkapalli, M. Sulgekar, S. Krithivasan, P. K. Das and A. Joshi, "Context Sensitive Access Control in Smart Home Environments," 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), Baltimore, MD, USA, 2020, pp. 35-41, doi: 10.1109/BigDataSecurity-HPSC-IDS49724.2020.00018.en_US
dc.identifier.uri10.1109/BigDataSecurity-HPSC-IDS49724.2020.00018
dc.identifier.urihttp://hdl.handle.net/11603/19213
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rights© 2020 IEEE
dc.subjectUMBC Ebiquity Research Group
dc.titleContext Sensitive Access Control in Smart Home Environmentsen_US
dc.typeTexten_US

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