Leveraging Strategic Defense Techniques for Smart Home Pricing Cyberattacks

Smart home, which controls the end use of the power grid, has become a critical component in the smart grid infrastructure. In a smart home system, the advanced metering infrastructure (AMI) is used to connect smart meters with the power system and the communication system of a smart grid. The electricity pricing information is transmitted from the utility to the local community, and then broadcast through wired or wireless networks to each smart meter within AMI. In this work, the vulnerability of the above process 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. These pricing cyberattacks explore the interdependance between the transmitted electricity pricing in the communication system and the energy load in the power system, which are the first such cyberattacks in the smart home context. A single event defense technique which uses support vector regression and impact difference for detecting anomaly pricing is proposed against those pricing cyberattacks. Since the single event detection does not have a long term view of the cyberattack impact, its detection capability is significantly limited. Thus, a partially observable Markov decision process based long term defense technique is also developed, which has the ingredients such as belief state, model training and policy transfer graph to account for the cumulative cyberattack impact and the potential future impact. Our simulation results demonstrate that the pricing cyberattack can reduce the attacker’s bill by 34.3% at the cost of the increase of others’ bill by 7.9% on average. In addition, the pricing cyberattack can unbalance the energy load of the local power system as it increases the peak to average ratio by 35.7%. Furthermore, our simulation results demonstrate that the proposed long term defense technique has the detection accuracy of more than 97% with the significant reduction in peak to average ratio and bill compared to a natural heuristic algorithm which repeatedly uses the single event defense technique.