"Cyberthreat analysis and detection for energy theft in social networki" by Yang Liu and Shiyan Hu
 

Cyberthreat analysis and detection for energy theft in social networking of smart homes

Document Type

Article

Publication Date

12-1-2015

Abstract

© 2014 IEEE. The advanced metering infrastructure (AMI) has become indispensable in a smart grid to support the real time and reliable information exchange. Such an infrastructure facilitates the deployment of smart meters and enables the automatic measurement of electricity energy usage. Inside a community of networked smart homes, the total electricity bill is computed based on the community-wide energy consumption. Thus, the coordinated energy scheduling among smart homes is important since the energy consumptions from some customers can potentially impact bills of others. Given a community of networked smart homes, this paper analyzes the energy theft cyberattack, which manipulates the energy usage metering for bill reduction and develops a detection technique based on Bollinger bands and partially observable Markov decision process (POMDP). Due to the high complexity of the POMDP-solving process, a probabilistic belief-state-reduction-based adaptive dynamic programming technique is also designed to improve the detection efficiency. Our simulation results demonstrate that the proposed technique can successfully detect 92.55% energy thefts on an average while effectively mitigating the impact to the community. In addition, our probabilistic belief-state-reduction-based adaptive dynamic programming technique can reduce the runtime by up to 55.86% compared to that without state reduction.

Publication Title

IEEE Transactions on Computational Social Systems

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