Biblio
With the continuously development of smart meter-reading technologies for decades, remote information collection of electricity, water, gas and heat meters have been realized. Due to the difference of electrical interfaces and communication protocols among various types of meters, communication modes of meter terminals are not so compatible, it is difficult to realize communication optimization of electricity, water, gas and heat meters information collection services. In addition, with the development of power consumption information acquisition system, the number of acquisition terminals soars greatly and the data of terminal access is highly concurrent. Therefore, the risk of security access is increasing. This paper presents a light-weighted security access scheme of power line communication based on multi-source data acquisition of electricity, water, gas and heat meters, which separates multi-source data acquisition services and achieve services security isolation and channel security isolation. The communication reliability and security of the meter-reading service of "electricity, water, gas and heat" will be improved and the integrated meter service will be realized reliably.
An advanced metering infrastructure (AMI) allows real-time fine-grained monitoring of the energy consumption data of individual consumers. Collected metering data can be used for a multitude of applications. For example, energy demand forecasting, based on the reported fine-grained consumption, can help manage the near future energy production. However, fine- grained metering data reporting can lead to privacy concerns. It is, therefore, imperative that the utility company receives the fine-grained data needed to perform the intended demand response service, without learning any sensitive information about individual consumers. In this paper, we propose an anonymous privacy preserving fine-grained data aggregation scheme for AMI networks. In this scheme, the utility company receives only the distribution of the energy consumption by the consumers at different time slots. We leverage a network tree topology structure in which each smart meter randomly reports its energy consumption data to its parent smart meter (according to the tree). The parent node updates the consumption distribution and forwards the data to the utility company. Our analysis results show that the proposed scheme can preserve the privacy and security of individual consumers while guaranteeing the demand response service.
Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.
The paper deals with the implementation aspects of the bilinear pairing operation over an elliptic curve on constrained devices, such as smart cards, embedded devices, smart meters and similar devices. Although cryptographic constructions, such as group signatures, anonymous credentials or identity-based encryption schemes, often rely on the pairing operation, the implementation of such schemes into practical applications is not straightforward, in fact, it may become very difficult. In this paper, we show that the implementation is difficult not only due to the high computational complexity, but also due to the lack of cryptographic libraries and programming interfaces. In particular, we show how difficult it is to implement pairing-based schemes on constrained devices and show the performance of various libraries on different platforms. Furthermore, we show the performance estimates of fundamental cryptographic constructions, the group signatures. The purpose of this paper is to reduce the gap between the cryptographic designers and developers and give performance results that can be used for the estimation of the implementability and performance of novel, upcoming schemes.
The rapid growth of population and industrialization has given rise to the way for the use of technologies like the Internet of Things (IoT). Innovations in Information and Communication Technologies (ICT) carries with it many challenges to our privacy's expectations and security. In Smart environments there are uses of security devices and smart appliances, sensors and energy meters. New requirements in security and privacy are driven by the massive growth of devices numbers that are connected to IoT which increases concerns in security and privacy. The most ubiquitous threats to the security of the smart grids (SG) ascended from infrastructural physical damages, destroying data, malwares, DoS, and intrusions. Intrusion detection comprehends illegitimate access to information and attacks which creates physical disruption in the availability of servers. This work proposes an intrusion detection system using data mining techniques for intrusion detection in smart grid environment. The results showed that the proposed random forest method with a total classification accuracy of 98.94 %, F-measure of 0.989, area under the ROC curve (AUC) of 0.999, and kappa value of 0.9865 outperforms over other classification methods. In addition, the feasibility of our method has been successfully demonstrated by comparing other classification techniques such as ANN, k-NN, SVM and Rotation Forest.
In rural/remote areas, resource constrained smart micro-grid (RCSMG) architectures can offer a cost-effective power management and supply alternative to national power grid connections. RCSMG architectures handle communications over distributed lossy networks to minimize operation costs. However, the unreliable nature of lossy networks makes privacy an important consideration. Existing anonymisation works on data perturbation work mainly by distortion with additive noise. Apply these solutions to RCSMGs is problematic, because deliberate noise additions must be distinguishable both from system and adversarial generated noise. In this paper, we present a brief survey of privacy risks in RCSMGs centered on inference, and propose a method of mitigating these risks. The lesson here is that while RCSMGs give users more control over power management and distribution, good anonymisation is essential to protecting personal information on RCSMGs.
The increasing deployment of smart meters at individual households has significantly improved people's experience in electricity bill payments and energy savings. It is, however, still challenging to guarantee the accurate detection of attacked meters' behaviors as well as the effective preservation of users'privacy information. In addition, rare existing research studies jointly consider both these two aspects. In this paper, we propose a Privacy-Preserving energy Theft Detection scheme (PPTD) to address the energy theft behaviors and information privacy issues in smart grid. Specifically, we use a recursive filter based on state estimation to estimate the user's energy consumption, and detect the abnormal data. During data transmission, we use the lightweight NTRU algorithm to encrypt the user's data to achieve privacy preservation. Security analysis demonstrates that in the PPTD scheme, only authorized units can transmit/receive data, and data privacy are also preserved. The performance evaluation results illustrate that our PPTD scheme can significantly reduce the communication and computation costs, and effectively detect abnormal users.
In the smart grid, residents' electricity usage needs to be periodically measured and reported for the purpose of better energy management. At the same time, real-time collection of residents' electricity consumption may unfavorably incur privacy leakage, which has motivated the research on privacy-preserving aggregation of electricity readings. Most previous studies either rely on a trusted third party (TTP) or suffer from expensive computation. In this paper, we first reveal the privacy flaws of a very recent scheme pursing privacy preservation without relying on the TTP. By presenting concrete attacks, we show that this scheme has failed to meet the design goals. Then, for better privacy protection, we construct a new scheme called PMDA, which utilizes Shamir's secret sharing to allow smart meters to negotiate aggregation parameters in the absence of a TTP. Using only lightweight cryptography, PMDA efficiently supports multi-functional aggregation of the electricity readings, and simultaneously preserves residents' privacy. Theoretical analysis is provided with regard to PMDA's security and efficiency. Moreover, experimental data obtained from a prototype indicates that our proposal is efficient and feasible for practical deployment.
With the advancement of Technology, the existing electric grids are shifting towards smart grid. The smart grids are meant to be effective in power management, secure and safe in communication and more importantly, it is favourable to the environment. The smart grid is having huge architecture it includes various stakeholders that encounter challenges in the name of authorisation and authentication. The smart grid has another important issue to deal with that is securing the communication from varieties of cyber-attacks. In this paper, we first discussed about the challenges in the smart grid data communication and later we surveyed the existing cryptographic algorithm and presented comparative work on certain factors for existing working cryptographic algorithms This work gives insight conclusion to improve the working scheme for data security and Privacy preservation of customer who is one of the stack holders. Finally, with the comparative work, we suggest a direction of future work on improvement of working algorithms for secure and safe data communication in a smart grid.
Nowadays, electricity companies have started applying smart grid in their systems rather than the conventional electrical grid (manual grid). Smart grid produces an efficient and effective energy management and control, reduces the cost of production, saves energy and it is more reliable compared to the conventional grid. As an advanced energy meter, smart meters can measure the power consumption as well as monitor and control electrical devices. Smart meters have been adopted in many countries since the 2000s as they provide economic, social and environmental benefits for multiple stakeholders. The design of smart meter can be customized depending on the customer and the utility company needs. There are different sensors and devices supported by dedicated communication infrastructure which can be utilized to implement smart meters. This paper presents a study of the challenges associated with smart meters, smart homes and smart grids as an effort to highlight opportunities for emerging research and industrial solutions.
Electronic power grid is a distributed network used for transferring electricity and power from power plants to consumers. Based on sensor readings and control system signals, power grid states are measured and estimated. As a result, most conventional attacks, such as denial-of-service attacks and random attacks, could be found by using the Kalman filter. However, false data injection attacks are designed against state estimation models. Currently, distributed Kalman filtering is proved effective in sensor networks for detection and estimation problems. Since meters are distributed in smart power grids, distributed estimation models can be used. Thus in this paper, we propose a diffusion Kalman filter for the power grid to have a good performance in estimating models and to effectively detect false data injection attacks.
Advanced Metering Infrastructure (AMI) forms a communication network for the collection of power data from smart meters in Smart Grid. As the communication within an AMI needs to be secure, key management becomes an issue due to overhead and limited resources. While using public-keys eliminate some of the overhead of key management, there is still challenges regarding certificates that store and certify the public-keys. In particular, distribution and storage of certificate revocation list (CRL) is major a challenge due to cost of distribution and storage in AMI networks which typically consist of wireless multi-hop networks. Motivated by the need of keeping the CRL distribution and storage cost effective and scalable, in this paper, we present a distributed CRL management model utilizing the idea of distributed hash trees (DHTs) from peer-to-peer (P2P) networks. The basic idea is to share the burden of storage of CRLs among all the smart meters by exploiting the meshing capability of the smart meters among each other. Thus, using DHTs not only reduces the space requirements for CRLs but also makes the CRL updates more convenient. We implemented this structure on ns-3 using IEEE 802.11s mesh standard as a model for AMI and demonstrated its superior performance with respect to traditional methods of CRL management through extensive simulations.
Since the Information Networks are added to the current electricity networks, the security and privacy of individuals is challenged. This combination of technologies creates vulnerabilities in the context of smart grid power which disrupt the consumer energy supply. Methods based on encryption are against the countermeasures attacks that have targeted the integrity and confidentiality factors. Although the cryptography strategies are used in Smart Grid, key management which is different in size from tens to millions of keys (for meters), is considered as the critical processes. The Key mismanagement causes to reveal the secret keys for attacker, a symmetric key distribution method is recently suggested by [7] which is based on a symmetric key distribution, this strategy is very suitable for smart electric meters. The problem with this method is its vulnerability to impersonating respondents attack. The proposed approach to solve this problem is to send the both side identifiers in encrypted form based on hash functions and a random value, the proposed solution is appropriate for devices such as meters that have very little computing power.
By applying power usage statistics from smart meters, users are able to save energy in their homes or control smart appliances via home automation systems. However, owing to security and privacy concerns, it is recommended that smart meters (SM) should not have direct communication with smart appliances. In this paper, we propose a design for a smart meter gateway (SMGW) associated with a two-phase authentication mechanism and key management scheme to link a smart grid with smart appliances. With placement of the SMGW, we can reduce the design complexity of SMs as well as enhance the strength of security.
The collection of high frequency metering data in the emerging smart grid gives rise to the concern of consumer privacy. Anonymization of metering data is one of the proposed approaches in the literature, which enables transmission of unmasked data while preserving the privacy of the sender. Distributed anonymization methods can reduce the dependency on service providers, thus promising more privacy for the consumers. However, the distributed communication among the end-users introduces overhead and requires methods to prevent external attacks. In this paper, we propose four variants of a distributed anonymization method for smart metering data privacy, referred to as the Collaborative Anonymity Set Formation (CASF) method. The performance overhead analysis and security analysis of the variants are done using NS-3 simulator and the Scyther tool, respectively. It is shown that the proposed scheme enhances the privacy preservation functionality of an existing anonymization scheme, while being robust against external attacks.
With the rapid development of smart grid, smart meters are deployed at energy consumers' premises to collect real-time usage data. Although such a communication model can help the control center of the energy producer to improve the efficiency and reliability of electricity delivery, it also leads to some security issues. For example, this real-time data involves the customers' privacy. Attackers may violate the privacy for house breaking, or they may tamper with the transmitted data for their own benefits. For this purpose, many data aggregation schemes are proposed for privacy preservation. However, rare of them cares about both the data aggregation and fine-grained access control to improve the data utility. In this paper, we proposes a data aggregation scheme based on attribute decision tree. Security analysis illustrates that our scheme can achieve the data integrity, data privacy preservation and fine- grained data access control. Experiment results show that our scheme are more efficient than existing schemes.
A smart grid is a fully automated power electricity network, which operates, protects and controls all its physical environments of power electricity infrastructure being able to supply energy in an efficient and reliable way. As the importance of cyber-physical system (CPS) security is growing, various vulnerability analysis methodologies for general systems have been suggested, whereas there has been few practical research targeting the smart grid infrastructure. In this paper, we highlight the significance of security vulnerability analysis in the smart grid environment. Then we introduce various automated vulnerability analysis techniques from executable files. In our approach, we propose a novel binary-based vulnerability discovery method for AMI and EV charging system to automatically extract security-related features from the embedded software. Finally, we present the test result of vulnerability discovery applied for AMI and EV charging system in Korean smart grid environment.
We propose a real time authentication scheme for smart grids which improves upon existing schemes. Our scheme is useful in many situations in smart grid operations. The smart grid Control Center (CC) communicates with the sensor nodes installed in the transmission lines so as to utilize real time data for monitoring environmental conditions in order to determine optimum power transmission capacity. Again a smart grid Operation Center (OC) communicates with several Residential Area (RA) gateways (GW) that are in turn connected to the smart meters installed in the consumer premises so as to dynamically control the power supply to meet demand based on real time electricity use information. It is not only necessary to authenticate sensor nodes and other smart devices, but also protect the integrity of messages being communicated. Our scheme is based on batch signatures and are more efficient than existing schemes. Furthermore our scheme is based on stronger notion of security, whereby the batch of signatures verify only if all individual signatures are valid. The communication overhead is kept low by using short signatures for verification.
In Advanced Metering Infrastructure (AMI) networks, power data collections from smart meters are static. Due to such static nature, attackers may predict the transmission behavior of the smart meters which can be used to launch selective jamming attacks that can block the transmissions. To avoid such attack scenarios and increase the resilience of the AMI networks, in this paper, we propose dynamic data reporting schedules for smart meters based on the idea of moving target defense (MTD) paradigm. The idea behind MTD-based schedules is to randomize the transmission times so that the attackers will not be able to guess these schedules. Specifically, we assign a time slot for each smart meter and in each round we shuffle the slots with Fisher-Yates shuffle algorithm that has been shown to provide secure randomness. We also take into account the periodicity of the data transmissions that may be needed by the utility company. With the proposed approach, a smart meter is guaranteed to send its data at a different time slot in each round. We implemented the proposed approach in ns-3 using IEEE 802.11s wireless mesh standard as the communication infrastructure. Simulation results showed that our protocol can secure the network from the selective jamming attacks without sacrificing performance by providing similar or even better performance for collection time, packet delivery ratio and end-to-end delay compared to previously proposed protocols.
Conventional intrusion detection systems for smart grid communications rely heavily on static based attack detection techniques. In essence, signatures created from historical data are compared to incoming network traffic to identify abnormalities. In the case of attacks where no historical data exists, static based approaches become ineffective thus relinquishing system resilience and stability. Moving target defense (MTD) has shown to be effective in discouraging attackers by introducing system entropy to increase exploit costs. Increase in exploit cost leads to a decrease in profitability for an attacker. In this paper, a Moving Target Defense Intrusion Detection System (MTDIDS) is proposed for smart grid IPv6 based advanced metering infrastructure. The advantage of MTDIDS is the ability to detect anomalies across moving targets by means of planar keys thereupon increasing detection rate. Evaluation of MTDIDS was carried out in a smart grid advanced metering infrastructure simulated in MATLAB.
In Germany, as of 2017, a new smart metering infrastructure based on high security and privacy requirements will be deployed. It provides interfaces to connect meters for different commodities, to allow end users to retrieve the collected measurement data, to connect to the metering operators, and to connect Controllable Local Systems (CLSs) that establish a TLS secured connection to third parties in order to exchange data or for remote controlling of energy devices. This paper aims to connect industrial machines as CLS devices since it shows that the demands and main ideas of remotely controlled devices in the Smart Grid context and Industrial Cloud Applications match on the communication level. It describes the general architecture of the Smart Metering infrastructure in Germany, introduces the defined roles, depicts the configuration process on the different organizational levels, demonstrates the connection establishment and the initiating partners, concludes on the potential industrial use cases of this infrastructure, and provides open questions and room for further research.
In this paper, we introduce a secure energy trading auction approach to schedule the power plant limited resources during peak hours time slots. In the proposed auction model, the power plant serving a power grid shares with the smart meters its available amount of resources that is expected during the next future peak time slot; smart meters expecting a demand for additional power participate in the power auction by submitting bids of their offered price for their requested amount of power. In order to secure the power auction and protect smart meters' privacy, homomorphic encryption through Paillier cryptosystem is used to secure the bidding values and ensure avoiding possible insincere behaviors of smart meters or the grid operator (i.e. the auctioneer) to manipulate the auction for their own benefits. In addition, we use a payment rule that maximizes the power plant's revenue. We propose an efficient power scheduling mechanism to distribute the operator's limited resources among smart meters participating in the power auction. Finally, we present simulation results for the performance of our secure power scheduling auction mechanism.
This paper proposes a novel privacy-preserving smart metering system for aggregating distributed smart meter data. It addresses two important challenges: (i) individual users wish to publish sensitive smart metering data for specific purposes, and (ii) an untrusted aggregator aims to make queries on the aggregate data. We handle these challenges using two main techniques. First, we propose Fourier Perturbation Algorithm (FPA) and Wavelet Perturbation Algorithm (WPA) which utilize Fourier/Wavelet transformation and distributed differential privacy (DDP) to provide privacy for the released statistic with provable sensitivity and error bounds. Second, we leverage an exponential ElGamal encryption mechanism to enable secure communications between the users and the untrusted aggregator. Standard differential privacy techniques perform poorly for time-series data as it results in a Θ(n) noise to answer n queries, rendering the answers practically useless if n is large. Our proposed distributed differential privacy mechanism relies on Gaussian principles to generate distributed noise, which guarantees differential privacy for each user with O(1) error, and provides computational simplicity and scalability. Compared with Gaussian Perturbation Algorithm (GPA) which adds distributed Gaussian noise to the original data, the experimental results demonstrate the superiority of the proposed FPA and WPA by adding noise to the transformed coefficients.
Advanced Metering Infrastructure (AMI) have rapidly become a topic of international interest as governments have sponsored their deployment for the purposes of utility service reliability and efficiency, e.g., water and electricity conservation. Two problems plague such deployments. First is the protection of consumer privacy. Second is the problem of huge amounts of data from such deployments. A new architecture is proposed to address these problems through the use of Aggregators, which incorporate temporary data buffering and the modularization of utility grid analysis. These Aggregators are used to deliver anonymized summary data to the central utility while preserving billing and automated connection services.