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dc.contributor.authorLi, Fengjun
dc.contributor.authorYang, Lei
dc.date.accessioned2018-12-13T19:46:17Z
dc.date.available2018-12-13T19:46:17Z
dc.date.issued2017-01-14
dc.identifier.urihttp://hdl.handle.net/1808/27502
dc.description.abstractExisting prevention-based secure in-network data aggregation schemes for the smart grids cannot e ectively detect accidental errors and falsified data injected by malfunctioning or compromised meters. In this work, we develop a light-weight anomaly detector based on kernel density estimator to locate the smart meter from which the falsified data is injected. To reduce the overhead at the collector, we design a dynamic grouping scheme, which divides meters into multiple interconnected groups and distributes the verification and detection load among the root of the groups. To enable outlier detection at the root of the groups, we also design a novel data re-encryption scheme based on bilinear mapping so that data previously encrypted using the aggregation key is transformed in a form that can be recovered by the outlier detectors using a temporary re-encryption key. Therefore, our proposed detection scheme is compatible with existing in-network aggregation approaches based on additive homomorphic encryption. We analyze the security and eÿciency of our scheme in terms of storage, computation and communication overhead, and evaluate the performance of our outlier detector with experiments using real-world smart meter consumption data. The results show that the performance of the light-weight detector yield high precision and recall.en_US
dc.publisherEuropean Alliance for Innovationen_US
dc.rightsCopyright © 2017 Lei Yang and Fengjun Li, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en_US
dc.titleSecuring Smart Grid In-Network Aggregation through False Data Detectionen_US
dc.typeArticleen_US
kusw.kudepartmentCivil, Environmental & Architectural Engineeringen_US
dc.identifier.doi10.4108/eai.1-2-2017.152156en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.rights.accessrightsopenAccess


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Copyright © 2017 Lei Yang and Fengjun Li, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
Except where otherwise noted, this item's license is described as: Copyright © 2017 Lei Yang and Fengjun Li, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.