Enhancing the security of a cloud‐based smart grid AMI network by leveraging on the features of quantum key distribution

Abstract

In the last decade, there have been many research proposals on the potentials of incorporating cloud computing technology to the electrical power grid as it evolves rapidly to the smart grid. The advanced metering infrastructure (AMI) is one domain of the smart grid that can be greatly impacted by cloud computing technology with regards to an improved capability for data storage and computations, which will be a serious burden to the AMI in an evolved smart grid environment. In our previous study, a cloud‐based OpenFlow firewall for the mitigation of distributed denial‐of‐service attacks in a smart grid AMI (SG AMI) was proposed. In that study, it was clearly demonstrated that cloud computing technology, in addition to improving the storage and computation capability of the AMI, can also be leveraged on to enhance the security of the SG AMI in an attack scenario. In this study, we propose a cloud‐based SG AMI system model utilizing quantum key distribution (QKD) cryptography for enhancing the security of a cloud‐based SG AMI. The proposed model has been designed to complement the security features of the previously designed OpenFlow firewall to guarantee not only the availability but also the confidentiality and integrity of data traversing the AMI network and beyond. The analysis on computation and communication costs shows that the protocols utilized in the proposed system model are lightweight protocols. In addition, results from the simulation of the QKD (BB84) protocol show that QKD can be leveraged on to provide information theoretic security for the cloud‐based SG AMI.

A Review of Quantum Key Distribution Protocols in the Perspective of Smart Grid Communication Security

“…For cost efficiency, a group of smart meters which are connected to a same aggregator may share a single quantum channel and a same QKD-generated secret key [69]. However, this will compromise the unconditional security strength brought by QKD protocols, because each individual secret key is known by more than a single pair of sender and receiver nodes.…”

“…Similar to [76] and [69] wants to protect the data privacy of individual consumers, but for the case where smart meters are not connected directly to the control center. In [69], smart meters are organized into multiple groups, where all smart meters within a group are connected directly only to a data aggregator. As illustrated in Fig.…”

“…6, the aggregator is further connected to a meter server, which is a proxy to the control center. Following the same choice of [76], the method proposed in [69] uses BB84 to establish a secret key between the control center and a meter server. Then, the meter server shares the same secret key with all its smart meters only, while keeping the key unknown to the aggregator.…”

“…However, in the absence of quantum channels between the meter server and smart meters, it is unclear how secret key can be secured distributed to all meters. Compared to [76] and [69] is less secure because it has contradicted the fundamental of QKD security, where a secret key can only be known by a pair of communicating nodes. With multiple smart meters knowing a same secret key, it is easier for at least one of the them to turn adversary or to disclose the key and thus, compromise the entire system.…”

Electricity theft detection by sources of threats for smart city planning

“…Some other reported techniques in literature include support vector machine, deep recurrent neural network (RNN), Lempel-Ziv algorithms, regression models, feasible generalised least squares models, integrated embedded system designed and developed for automation of the substation as a measure for clearing power theft, application of wireless data transmission and reception techniques employed for power monitoring and theft detection, a quantum key distribution model for a cloud-based security of the AMI, GSMbased prepaid energy meter presented to curb meter bypass, temperature-dependent predictive model which uses smart meter data and data from distribution transformer to detect electricity theft in an area, reconstructed user load profile using compressed distributed sensing based on random walk to demonstrate monitoring of customer's consumption for possible electricity theft, advanced wireless technology for monitoring of power theft, predictive models for TLs in electrical distribution networks [4,19,38,41,49,[51][52][53][54][55][56][57] etc.…”