Exploiting DLMS/COSEM Data Compression To Learn Power Consumption Patterns

Marcell Fehér, Daniel Enrique Lucani Rötter, Morten Tranberg Hansen, Flemming Enevold Vester

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

Smart electricity meters are convenient and useful for uploading power consumption readings automatically and frequently. However, the legacy compression methods have been suspected to leak information about when tenants are active by mirroring spikes of power consumption in the compressed message size. In this paper we show that, compressed message sizes are indeed highly correlated with the underlying power use. We present a clustering-based method that allows a passive adversary monitoring encrypted network traffic to build and exploit power consumption profiles of homes. We evaluate the vulnerability of legacy compressors of the DLMS/COSEM standard as well as a recently proposed algorithm. Our results show that the novel algorithm not only provides higher compression potential, but results in the least information leakage. We evaluate our results on an real, anonymized dataset spanning 9 months and 95 households.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2021
Number of pages6
PublisherIEEE
Publication date13 Dec 2021
Pages346-351
ISBN (Electronic)978-1-6654-1502-6
DOIs
Publication statusPublished - 13 Dec 2021

Keywords

  • compression
  • dlms
  • generalized deduplication
  • iot
  • smart meter

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  • Scale-loT

    Lucani Rötter, D. E.

    01/01/201831/12/2022

    Project: Research

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