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dc.contributor.authorShifa, Amna
dc.contributor.authorAsghar, Mamoona Naveed
dc.contributor.authorAhmed, Adeel
dc.contributor.authorMartin, Fleury
dc.identifier.citationShifa, A., Naveed Asghar, M., Ahmed, A. Fleury, M. (2020). Fuzzy-logic threat classification for multi-level selective encryption over real-time video streams. Journal of Ambient Intelligence and Humanized Computing. April 2020.
dc.identifier.otherArticles - Software Research Institute AITen_US
dc.description.abstractThis paper proposes a Fuzzy-logic Threat Classification (FTC) model as the basis of a method to auto-detect three different confidentiality levels for videos streamed from heterogeneous, mobile devices via web edge servers, possibly part of a Content Distribution Network (CDN). The FTC consists of three parallel Fuzzy Inference Systems (FIS) corresponding to device, network, and type of video application, for the real-time, intelligent selection of an appropriate confidentiality level for a specific end-user. After selection of the level, an encryption module implements the corresponding form of encryption. In tests to demonstrate the concept, there were three increasing confidentiality levels, namely (1) low-level with no encryption, (2) Medium level with an in-house cipher [variant of eXclusive OR (XOR)], named P-XOR (XOR with additional rounds of permutation) applied to Selective Encryption (SE) and (3) high level with the Advanced Encryption Standard again for SE of compressed video syntax components. Results were obtained by considering realistic specifications of multiple digital devices, networks, and differing real-time streaming applications. Visual analysis of encrypted test video clips established that the FTC outputs an appropriate privacy level by reason of the implemented FISs. Absolute encryption times across the privacy levels were distinguished by their real-time response level, which is proportionate to the required degree of confidentialityen_US
dc.relation.ispartofJournal of Ambient Intelligence and Humanized Computingen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland*
dc.subjectClassification modelen_US
dc.subjectEdge serversen_US
dc.subjectFuzzy rule-based systemen_US
dc.subjectHEVC and H.264/AVC codesen_US
dc.titleFuzzy‑logic threat classification for multi‑level selective encryption over real‑time video streams.en_US
dc.contributor.sponsorNational Research Program for Universities (NRPU-2016)/6282/Punjab/NRPU/R&D/HEC/2016.en_US
dc.rights.accessOpen Accessen_US
dc.subject.departmentSoftware Research Institute AITen_US

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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland