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Leveraging strategic detection techniques for smart home pricing cyberattacks

Leveraging strategic detection techniques for smart home pricing cyberattacks
Leveraging strategic detection techniques for smart home pricing cyberattacks
In this work, the vulnerability of the electricity pricing model in the smart home system is assessed. Two closely related pricing cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the cyberattacker and increasing the peak energy usage in the local community. A single event detection technique which uses support vector regression and impact difference for detecting anomaly pricing is proposed. The detection capability of such a technique is still limited since it does not model the long term impact of pricing cyberattacks. This motivates us to develop a partially observable Markov decision process based detection algorithm, which has the ingredients such as reward expectation and policy transfer graph to account for the cumulative impact and the potential future impact due to pricing cyberattacks. Our simulation results demonstrate that the pricing cyberattack can reduce the cyberattacker's bill by 34.3 percent at cost of the increase of others' bill by 7.9 percent, and increase the peak to average ratio (PAR) by 35.7 percent. Furthermore, the proposed long term detection technique has the detection accuracy of more than 97 percent with significant reduction in PAR and bill compared to repeatedly using the single event detection technique.
1545-5971
220-235
Liu, Yang
ee102c72-3e78-4685-9913-521fd608039d
Hu, Shiyan
19bb09b2-bf52-4bd7-818a-63e8da474072
Ho, Tsung-Yi
b84ce51e-f413-4cb2-bbea-992fc937d890
Liu, Yang
ee102c72-3e78-4685-9913-521fd608039d
Hu, Shiyan
19bb09b2-bf52-4bd7-818a-63e8da474072
Ho, Tsung-Yi
b84ce51e-f413-4cb2-bbea-992fc937d890

Liu, Yang, Hu, Shiyan and Ho, Tsung-Yi (2016) Leveraging strategic detection techniques for smart home pricing cyberattacks. IEEE Transactions on Dependable and Secure Computing, 13 (2), 220-235, [7115920]. (doi:10.1109/TDSC.2015.2427841).

Record type: Article

Abstract

In this work, the vulnerability of the electricity pricing model in the smart home system is assessed. Two closely related pricing cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the cyberattacker and increasing the peak energy usage in the local community. A single event detection technique which uses support vector regression and impact difference for detecting anomaly pricing is proposed. The detection capability of such a technique is still limited since it does not model the long term impact of pricing cyberattacks. This motivates us to develop a partially observable Markov decision process based detection algorithm, which has the ingredients such as reward expectation and policy transfer graph to account for the cumulative impact and the potential future impact due to pricing cyberattacks. Our simulation results demonstrate that the pricing cyberattack can reduce the cyberattacker's bill by 34.3 percent at cost of the increase of others' bill by 7.9 percent, and increase the peak to average ratio (PAR) by 35.7 percent. Furthermore, the proposed long term detection technique has the detection accuracy of more than 97 percent with significant reduction in PAR and bill compared to repeatedly using the single event detection technique.

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e-pub ahead of print date: 1 June 2015
Published date: March 2016

Identifiers

Local EPrints ID: 438341
URI: http://eprints.soton.ac.uk/id/eprint/438341
ISSN: 1545-5971
PURE UUID: aba18c9e-19e3-4f7d-99ef-d819ece0a2a0

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Date deposited: 06 Mar 2020 17:30
Last modified: 08 Feb 2021 17:30

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