Exploiting DLMS/COSEM Data Compression To Learn Power Consumption Patterns

TY - GEN

T1 - Exploiting DLMS/COSEM Data Compression To Learn Power Consumption Patterns

AU - Fehér, Marcell

AU - Lucani Rötter, Daniel Enrique

AU - Hansen, Morten Tranberg

AU - Vester, Flemming Enevold

PY - 2021/12/13

Y1 - 2021/12/13

N2 - Smart electricity meters are widely deployed report power consumption automatically and frequently. However, the current compression methods have been suspected to leak information about the times when consumers 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.

AB - Smart electricity meters are widely deployed report power consumption automatically and frequently. However, the current compression methods have been suspected to leak information about the times when consumers 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.

KW - compression

KW - dlms

KW - generalized deduplication

KW - iot

KW - smart meter

UR - http://www.scopus.com/inward/record.url?scp=85123946367&partnerID=8YFLogxK

U2 - 10.1109/SmartGridComm51999.2021.9632291

DO - 10.1109/SmartGridComm51999.2021.9632291

M3 - Article in proceedings

SP - 346

EP - 351

BT - 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2021

PB - IEEE

ER -