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 rule of thumb electricity costs received at sensible meters are considered and that they aim at reducing the expense of the cyberattacker and increasing the height energy usage in the local people. One event detection technique that uses support vector regression and impact difference for detecting anomaly pricing is proposed. The detection capability of such a way remains restricted since it does not model the long term impact of pricing cyberattacks. This motivates us to develop a partially observable Markov decision method primarily based detection algorithm, which has the ingredients such as reward expectation and policy transfer graph to account for the cumulative impact and therefore the potential future impact thanks to pricing cyberattacks. Our simulation results demonstrate that the pricing cyberattack can reduce the cyberattacker's bill by thirty four.3 percent at value of the rise of others' bill by 7.nine p.c, and increase the height to average ratio (PAR) by thirty p.c. Furthermore, the proposed long run detection technique has the detection accuracy of a lot of than ninety seven p.c with important reduction in PAR and bill compared to repeatedly using the single event detection technique.

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