EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 17(10): e5

Research Article

Securing Smart Grid In-Network Aggregation through False Data Detection

Download61 downloads
  • @ARTICLE{10.4108/eai.1-2-2017.152156,
        author={Lei Yang and Fengjun Li},
        title={Securing Smart Grid In-Network Aggregation through False Data Detection},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={17},
        number={10},
        publisher={EAI},
        journal_a={INIS},
        year={2017},
        month={2},
        keywords={Smart grid, security, anomaly detection},
        doi={10.4108/eai.1-2-2017.152156}
    }
    
  • Lei Yang
    Fengjun Li
    Year: 2017
    Securing Smart Grid In-Network Aggregation through False Data Detection
    INIS
    EAI
    DOI: 10.4108/eai.1-2-2017.152156
Lei Yang1, Fengjun Li1,*
  • 1: Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence,KS, 66045, US
*Contact email: fli@ku.edu

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.