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