Securing Smart Grid In-Network Aggregation through False Data Detection
Journal Title: EAI Endorsed Transactions on Industrial Networks and Intelligent Systems - Year 2017, Vol 4, Issue 10
Abstract
Existing 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.
Authors and Affiliations
Lei Yang, Fengjun Li
Keywords
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- EP ID EP46061
- DOI http://dx.doi.org/10.4108/eai.1-2-2017.152156
- Views 306
- Downloads 0
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