Biblio
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.
Smart grid is the cornerstone of the modern urban construction, leading the development trend of the urban power industry. Wireless sensor network (WSN) is widely used in smart power grid. It mainly covers two routing methods, the plane routing protocol and the clustering routing protocol. Since the plane routing protocol needs to maintain a large routing table and works with a poor scalability, it will increase the overall cost of the system in practical use. Therefore, in this paper, the clustering routing protocol is selected to achieve a better operation performance of the wireless sensor network. In order to enhance the reliability of the routing security, the data fusion technology is also utilized. Based on this method, the rationality of the topology structure of the smart grid and the security of the node information can be effectively improved.
Smart Grid cybersecurity is one of the key ingredients for successful and wide scale adaptation of the Smart Grid by utilities and governments around the world. The implementation of the Smart Grid relies mainly on the highly distributed sensing and communication functionalities of its components such as Wireless Sensor Networks (WSNs), Phasor Measurement Units (PMUs) and other protection devices. This distributed nature and the high number of connected devices are the main challenges for implementing cybersecurity in the smart grid. As an example, the North American Electric Reliability Corporation (NERC) issued the Critical Infrastructure Protection (CIP) standards (CIP-002 through CIP-009) to define cybersecurity requirements for critical power grid infrastructure. However, NERC CIP standards do not specify cybersecurity for different communication technologies such as WSNs, fiber networks and other network types. Implementing security mechanisms in WSNs is a challenging task due to the limited resources of the sensor devices. WSN security mechanisms should not only focus on reducing the power consumption of the sensor devices, but they should also maintain high reliability and throughput needed by Smart Grid applications. In this paper, we present a WSN cybersecurity mechanism suitable for smart grid monitoring application. Our mechanism can detect and isolate various attacks in a smart grid environment, such as denial of sleep, forge and replay attacks in an energy efficient way. Simulation results show that our mechanism can outperform existing techniques while meeting the NERC CIP requirements.
Traditional security measures for large-scale critical infrastructure systems have focused on keeping adversaries out of the system. As the Internet of Things (IoT) extends into millions of homes, with tens or hundreds of devices each, the threat landscape is complicated. IoT devices have unknown access capabilities with unknown reach into other systems. This paper presents ongoing work on how techniques in sensor verification and cyber-physical modeling and analysis on bulk power systems can be applied to identify malevolent IoT devices and secure smart and connected communities against the most impactful threats.
The eleven papers in this special section focus on power electronics-enabled autonomous systems. Power systems are going through a paradigm change from centralized generation to distributed generation and further onto smart grid. Millions of relatively small distributed energy resources (DER), including wind turbines, solar panels, electric vehicles and energy storage systems, and flexible loads are being integrated into power systems through power electronic converters. This imposes great challenges to the stability, scalability, reliability, security, and resiliency of future power systems. This section joins the forces of the communities of control/systems theory, power electronics, and power systems to address various emerging issues of power-electronics-enabled autonomous power systems, paving the way for large-scale deployment of DERs and flexible loads.
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.
Integration of information technologies with the current power infrastructure promises something further than a smart grid: implementation of smart cities. Power efficient cities will be a significant step toward greener cities and a cleaner environment. However, the extensive use of information technologies in smart cities comes at a cost of reduced privacy. In particular, consumers' power profiles will be accessible by third parties seeking information over consumers' personal habits. In this paper, a methodology for enhancing privacy of electricity consumption patterns is proposed and tested. The proposed method exploits digital connectivity and predictive tools offered via smart grids to morph consumption patterns by grouping consumers via an optimization scheme. To that end, load anticipation, correlation and Theil coefficients are utilized synergistically with genetic algorithms to find an optimal assembly of consumers whose aggregated pattern hides individual consumption features. Results highlight the efficiency of the proposed method in enhancing privacy in the environment of smart cities.
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.
Power grid infrastructures have been exposed to several terrorists and cyber attacks from different perspectives and have resulted in critical system failures. Among different attack strategies, simultaneous attack is feasible for the attacker if enough resources are available at the moment. In this paper, vulnerability analysis for simultaneous attack is investigated, using a modified cascading failure simulator with reduced calculation time than the existing methods. A new damage measurement matrix is proposed with the loss of generation power and time to reach the steady-state condition. The combination of attacks that can result in a total blackout in the shortest time are considered as the strongest simultaneous attack for the system from attacker's viewpoint. The proposed approach can be used for general power system test cases. In this paper, we conducted the experiments on W&W 6 bus system and IEEE 30 bus system for demonstration of the result. The modified simulator can automatically find the strongest attack combinations for reaching maximum damage in terms of generation power loss and time to reach black-out.
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.
The previous consideration of power grid focuses on the power system itself, however, the recent work is aiming at both power grid and communication network, this coupling networks are firstly called as interdependent networks. Prior study on modeling interdependent networks always extracts main features from real networks, the model of network A and network B are completely symmetrical, both degree distribution in intranetwork and support pattern in inter-network, but in reality this circumstance is hard to attain. In this paper, we deliberately set both networks with same topology in order to specialized research the support pattern between networks. In terms of initial failure from power grid or communication network, we find the remaining survival fraction is greatly disparate, and the failure initially from power grid is more harmful than failure initially from communication network, which all show the vulnerability of interdependency and meantime guide us to pay more attention to the protection measures for power grid.
The false data injection attack (FDIA) is a form of cyber-attack capable of affecting the secure and economic operation of the smart grid. With DC model-based state estimation, this paper analyzes ways of constructing a successful attacking vector to fulfill specific targets, i.e., pre-specified state variable target and pre-specified meter target according to the adversary's willingness. The grid operator's historical reading experiences on meters are considered as a constraint for the adversary to avoid being detected. Also from the viewpoint of the adversary, we propose to take full advantage of the dual concept of the coefficients in the topology matrix to handle with the problem that the adversary has no access to some meters. Effectiveness of the proposed method is validated by numerical experiments on the IEEE-14 benchmark system.
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.
This research in progress paper describes the role of cyber security measures undertaken in an ICT system for integrating electric storage technologies into the grid. To do so, it defines security requirements for a communications gateway and gives detailed information and hands-on configuration advice on node and communication line security, data storage, coping with backend M2M communications protocols and examines privacy issues. The presented research paves the road for developing secure smart energy communications devices that allow enhancing energy efficiency. The described measures are implemented in an actual gateway device within the HORIZON 2020 project STORY, which aims at developing new ways to use storage and demonstrating these on six different demonstration sites.
Protection of information achieves keeping confidentiality, integrity, and availability of the data. These features are essential for the proper operation of modern industrial technologies, like Smart Grid. The complex grid system integrates many electronic devices that provide an efficient way of exploiting the power systems but cause many problems due to their vulnerabilities to attacks. The aim of the work is to propose a solution to the privacy problem in Smart Grid communication network between the customers and Control center. It consists in using the relatively new cryptographic task - quantum key distribution (QKD). The solution is based on choosing an appropriate quantum key distribution method out of all the conventional ones by performing an assessment in terms of several parameters. The parameters are: key rate, operating distances, resources, and trustworthiness of the devices involved. Accordingly, we discuss an answer to the privacy problem of the SG network with regard to both security and resource economy.
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.
Security is an important requirement of every reactive system of the smart gird. The devices connected to the smart system in smart grid are exhaustively used to provide digital information to outside world. The security of such a system is an essential requirement. The most important component of such smart systems is Operating System (OS). This paper mainly focuses on the security of OS by incorporating Access Control Mechanism (ACM) which will improve the efficiency of the smart system. The formal methods use applied mathematics for modelling and analysing of smart systems. In the proposed work Formal Security Analysis (FSA) is used with model checking and hence it helped to prove the security of smart systems. When an Operating System (OS) takes into consideration, it never comes to a halt state. In the proposed work a Transition System (TS) is designed and the desired rules of security are provided by using Linear Temporal Logics (LTL). Unlike other propositional and predicate logic, LTL can model reactive systems with a prediction for the future state of the systems. In the proposed work, Simple Promela Interpreter (SPIN) is used as a model checker that takes LTL and TS of the system as input. Hence it is possible to derive the Büchi automaton from LTL logics and that provides traces of both successful and erroneous computations. Comparison of Büchi automaton with the transition behaviour of the OS will provide the details of security violation in the system. Validation of automaton operations on infinite computational sequences verify that whether systems are provably secure or not. Hence the proposed formal security analysis will provably ensures the security of smart systems in the area of smart grid applications.
Software Defined Networks (SDNs) is a new networking paradigm that has gained a lot of attention in recent years especially in implementing data center networks and in providing efficient security solutions. The popularity of SDN and its attractive security features suggest that it can be used in the context of smart grid systems to address many of the vulnerabilities and security problems facing such critical infrastructure systems. This paper studies the impact of different cyber attacks that can target smart grid communication network which is implemented as a software defined network on the operation of the smart grid system in general. In particular, we perform different attack scenarios including DDoS attacks, location highjacking and link overloading against SDN networks of different controller types that include POX, Floodlight and RYU. Our experiments were carried out using the mininet simulator. The experiments show that SDN-enabled smartgrid systems are vulnerable to different types of attacks.
Recently, the researches utilizing environmentally friendly new and renewable energy and various methods have been actively pursued to solve environmental and energy problems. The trend of the technology is converged with the latest ICT technology and expanded to the cloud of share and two-way system. In the center of this tide of change, new technologies such as IoT, Big Data and AI are sustaining to energy technology. Now, the cloud concept which is a universal form in IT field will be converged with energy field to develop Energy Cloud, manage zero energy towns and develop into social infrastructure supporting smart city. With the development of social infrastructure, it is very important as a security facility. In this paper, it is discussed the concept and the configuration of the Energy Cloud, and present a basic design method of the Energy Cloud's security that can examine and respond to the risk factors of information security in the Energy Cloud.
Technological advancement enables the need of internet everywhere. The power industry is not an exception in the technological advancement which makes everything smarter. Smart grid is the advanced version of the traditional grid, which makes the system more efficient and self-healing. Synchrophasor is a device used in smart grids to measure the values of electric waves, voltages and current. The phasor measurement unit produces immense volume of current and voltage data that is used to monitor and control the performance of the grid. These data are huge in size and vulnerable to attacks. Intrusion Detection is a common technique for finding the intrusions in the system. In this paper, a big data framework is designed using various machine learning techniques, and intrusions are detected based on the classifications applied on the synchrophasor dataset. In this approach various machine learning techniques like deep neural networks, support vector machines, random forest, decision trees and naive bayes classifications are done for the synchrophasor dataset and the results are compared using metrics of accuracy, recall, false rate, specificity, and prediction time. Feature selection and dimensionality reduction algorithms are used to reduce the prediction time taken by the proposed approach. This paper uses apache spark as a platform which is suitable for the implementation of Intrusion Detection system in smart grids using big data analytics.
In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications is usually sensitive. For instance, in order to dispatch accurately and support the dynamic price, lots of smart meters are installed at user's house to collect the real-time data, but all these collected data are related to user privacy. In this paper, we propose a data aggregation scheme based on secret sharing with fault tolerance in smart grid, which ensures that control center gets the integrated data without revealing user's privacy. Meanwhile, we also consider fault tolerance during the data aggregation. At last, we analyze the security of our scheme and carry out experiments to validate the results.