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Wang, Chenguang, Pan, Kaikai, Tindemans, Simon, Palensky, Peter.  2020.  Training Strategies for Autoencoder-based Detection of False Data Injection Attacks. 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). :1—5.
The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has the ability to achieve satisfactory learning efficiency and detection accuracy.
Zhao, Yi, Jia, Xian, An, Dou, Yang, Qingyu.  2020.  LSTM-Based False Data Injection Attack Detection in Smart Grids. 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC). :638—644.
As a typical cyber-physical system, smart grid has attracted growing attention due to the safe and efficient operation. The false data injection attack against energy management system is a new type of cyber-physical attack, which can bypass the bad data detector of the smart grid to influence the results of state estimation directly, causing the energy management system making wrong estimation and thus affects the stable operation of power grid. We transform the false data injection attack detection problem into binary classification problem in this paper, which use the long-term and short-term memory network (LSTM) to construct the detection model. After that, we use the BP algorithm to update neural network parameters and utilize the dropout method to alleviate the overfitting problem and to improve the detection accuracy. Simulation results prove that the LSTM-based detection method can achieve higher detection accuracy comparing with the BPNN-based approach.
Lu, Xiao, Jing, Jiangping, Wu, Yi.  2020.  False Data Injection Attack Location Detection Based on Classification Method in Smart Grid. 2020 2nd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM). :133—136.
The state estimation technology is utilized to estimate the grid state based on the data of the meter and grid topology structure. The false data injection attack (FDIA) is an information attack method to disturb the security of the power system based on the meter measurement. Current FDIA detection researches pay attention on detecting its presence. The location information of FDIA is also important for power system security. In this paper, locating the FDIA of the meter is regarded as a multi-label classification problem. Each label represents the state of the corresponding meter. The ensemble model, the multi-label decision tree algorithm, is utilized as the classifier to detect the exact location of the FDIA. This method does not need the information of the power topology and statistical knowledge assumption. The numerical experiments based on the IEEE-14 bus system validates the performance of the proposed method.
Maneebang, Kotchakorn, Methapatara, Kanokpol, Kudtongngam, Jasada.  2020.  A Demand Side Management Solution: Fully Automated Demand Response using OpenADR2.0b Coordinating with BEMS Pilot Project. 2020 International Conference on Smart Grids and Energy Systems (SGES). :30–35.
Per the National Energy Policy, Demand Side Management (DSM) is one of the energy conservations that performs a function to manage electric power of demand-side resources. One of the DSM solutions is a demand response program, which is a part of Thailand Smart Grid Action Plan 2017 - 2021. Demand response program such as peak demand reduction plays a role in both the management of the electricity crisis and enhance energy security. This paper presents a pilot project for a fully automated demand response program at MEA Rat Burana District Office. The system is composed of a Building Energy Management System (BEMS) with Demand Response Client gateway and 5 energy controllers at the air conditioner by using the OpenADR2.0b protocol. Also, this concept leads to automatic or semi-automatic demand response program in the future. The result shows the total energy consumption reduction for air conditioners by 53.5%. The future works to be carried out are to implement into other MEA District Office such as Khlong Toei, Yan Nawa and Bang Khun Thian and to test with a Load Aggregator Management System (LAMS).
Shi, Jie, Foggo, Brandon, Kong, Xianghao, Cheng, Yuanbin, Yu, Nanpeng, Yamashita, Koji.  2020.  Online Event Detection in Synchrophasor Data with Graph Signal Processing. 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1—7.
Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.
Chinchawade, Amit Jaykumar, Lamba, Onkar Singh.  2020.  Authentication Schemes and Security Issues in Internet Of Everything (IOE) Systems. 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN). :342–345.
Nowadays, Internet Of Everything (IOE) has demanded for a wide range of applications areas. IOE is started to replaces an Internet Of things (IOT). IOE is a combination of massive number of computing elements and sensors, people, processes and data through the Internet infrastructure. Device to Device communication and interfacing of Wireless Sensor network with IOE can makes any system as a Smart System. With the increased the use of Internet and Internet connected devices has opportunities for hackers to launch attacks on unprecedented scale and impact. The IOE can serve the varied security in the various sectors like manufacturing, agriculture, smart grid, payments, IoT gateways, healthcare and industrial ecosystems. To secure connections among people, process, data, and things, is a major challenge in Internet of Everything.. This paper focuses on various security Issues and Authentication Schemes in the IOE systems.
Moslemi, Ramin, Davoodi, Mohammadreza, Velni, Javad Mohammadpour.  2020.  A Distributed Approach for Estimation of Information Matrix in Smart Grids and its Application for Anomaly Detection. 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1—7.

Statistical structure learning (SSL)-based approaches have been employed in the recent years to detect different types of anomalies in a variety of cyber-physical systems (CPS). Although these approaches outperform conventional methods in the literature, their computational complexity, need for large number of measurements and centralized computations have limited their applicability to large-scale networks. In this work, we propose a distributed, multi-agent maximum likelihood (ML) approach to detect anomalies in smart grid applications aiming at reducing computational complexity, as well as preserving data privacy among different players in the network. The proposed multi-agent detector breaks the original ML problem into several local (smaller) ML optimization problems coupled by the alternating direction method of multipliers (ADMM). Then, these local ML problems are solved by their corresponding agents, eventually resulting in the construction of the global solution (network's information matrix). The numerical results obtained from two IEEE test (power transmission) systems confirm the accuracy and efficiency of the proposed approach for anomaly detection.

Hopkins, Stephen, Kalaimannan, Ezhil, John, Caroline Sangeetha.  2020.  Cyber Resilience using State Estimation Updates Based on Cyber Attack Matrix Classification. 2020 IEEE Kansas Power and Energy Conference (KPEC). :1—6.
Cyber-physical systems (CPS) maintain operation, reliability, and safety performance using state estimation and control methods. Internet connectivity and Internet of Things (IoT) devices are integrated with CPS, such as in smart grids. This integration of Operational Technology (OT) and Information Technology (IT) brings with it challenges for state estimation and exposure to cyber-threats. This research establishes a state estimation baseline, details the integration of IT, evaluates the vulnerabilities, and develops an approach for detecting and responding to cyber-attack data injections. Where other approaches focus on integration of IT cyber-controls, this research focuses on development of classification tools using data currently available in state estimation methods to quantitatively determine the presence of cyber-attack data. The tools may increase computational requirements but provide methods which can be integrated with existing state estimation methods and provide for future research in state estimation based cyber-attack incident response. A robust cyber-resilient CPS includes the ability to detect and classify a cyber-attack, determine the true system state, and respond to the cyber-attack. The purpose of this paper is to establish a means for a cyber aware state estimator given the existence of sub-erroneous outlier detection, cyber-attack data weighting, cyber-attack data classification, and state estimation cyber detection.
Tian, Nianfeng, Guo, Qinglai, Sun, Hongbin, Huang, Jianye.  2020.  A Synchronous Iterative Method of Power Flow in Inter-Connected Power Grids Considering Privacy Preservation: A CPS Perspective. 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). :782–787.
The increasing development of smart grid facilitates that modern power grids inter-connect with each other and form a large power system, making it possible and advantageous to conduct coordinated power flow among several grids. The communication burden and privacy issue are the prominent challenges in the application of synchronous iteration power flow method. In this paper, a synchronous iterative method of power flow in inter-connected power grid considering privacy preservation is proposed. By establishing the masked model of power flow for each sub-grid, the synchronous iteration is conducted by gathering the masked model of sub-grids in the coordination center and solving the masked correction equation in a concentration manner at each step. Generally, the proposed method can concentrate the major calculation of power flow on the coordination center, reduce the communication burden and guarantee the privacy preservation of sub-grids. A case study on IEEE 118-bus test system demonstrate the feasibility and effectiveness of the proposed methodology.
Pradhan, Ankit, R., Punith., Sethi, Kamalakanta, Bera, Padmalochan.  2020.  Smart Grid Data Security using Practical CP-ABE with Obfuscated Policy and Outsourcing Decryption. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1–8.
Smart grid consists of multiple different entities related to various energy management systems which share fine-grained energy measurements among themselves in an optimal and reliable manner. Such delivery is achieved through intelligent transmission and distribution networks composed of various stakeholders like Phasor Measurement Units (PMUs), Master and Remote Terminal Units (MTU and RTU), Storage Centers and users in power utility departments subject to volatile changes in requirements. Hence, secure accessibility of data becomes vital in the context of efficient functioning of the smart grid. In this paper, we propose a practical attribute-based encryption scheme for securing data sharing and data access in Smart Grid architectures with the added advantage of obfuscating the access policy. This is aimed at preserving data privacy in the context of competing smart grid operators. We build our scheme on Linear Secret Sharing (LSS) Schemes for supporting any monotone access structures and thus enhancing the expressiveness of access policies. Lastly, we analyze the security, access policy privacy and collusion resistance properties of our cryptosystem and provide an efficiency comparison as well as experimental analysis using the Charm-Crypto framework to validate the proficiency of our proposed solution.
Fei, Wanghao, Moses, Paul, Davis, Chad.  2020.  Identification of Smart Grid Attacks via State Vector Estimator and Support Vector Machine Methods. 2020 Intermountain Engineering, Technology and Computing (IETC). :1—6.

In recent times, an increasing amount of intelligent electronic devices (IEDs) are being deployed to make power systems more reliable and economical. While these technologies are necessary for realizing a cyber-physical infrastructure for future smart power grids, they also introduce new vulnerabilities in the grid to different cyber-attacks. Traditional methods such as state vector estimation (SVE) are not capable of identifying cyber-attacks while the geometric information is also injected as an attack vector. In this paper, a machine learning based smart grid attack identification method is proposed. The proposed method is carried out by first collecting smart grid power flow data for machine learning training purposes which is later used to classify the attacks. The performance of both the proposed SVM method and the traditional SVE method are validated on IEEE 14, 30, 39, 57 and 118 bus systems, and the performance regarding the scale of the power system is evaluated. The results show that the SVM-based method performs better than the SVE-based in attack identification over a much wider scale of power systems.

Peng, X., Hongmei, Z., Lijie, C., Ying, H..  2020.  Analysis of Computer Network Information Security under the Background of Big Data. 2020 5th International Conference on Smart Grid and Electrical Automation (ICSGEA). :409—412.
In today's society, under the comprehensive arrival of the Internet era, the rapid development of technology has facilitated people's production and life, but it is also a “double-edged sword”, making people's personal information and other data subject to a greater threat of abuse. The unique features of big data technology, such as massive storage, parallel computing and efficient query, have created a breakthrough opportunity for the key technologies of large-scale network security situational awareness. On the basis of big data acquisition, preprocessing, distributed computing and mining and analysis, the big data analysis platform provides information security assurance services to the information system. This paper will discuss the security situational awareness in large-scale network environment and the promotion of big data technology in security perception.
Siu, J. Y., Panda, S. Kumar.  2020.  A Specification-Based Detection for Attacks in the Multi-Area System. IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. :1526—1526.
In the past decade, cyber-attack events on the power grid have proven to be sophisticated and advanced. These attacks led to severe consequences on the grid operation, such as equipment damage or power outages. Hence, it is more critical than ever to develop tools for security assessment and detection of anomalies in the cyber-physical grid. For an extensive power grid, it is complex to analyze the causes of frequency deviations. Besides, if the system is compromised, attackers can leverage on the frequency deviation to bypass existing protection measures of the grid. This paper aims to develop a novel specification-based method to detect False Data Injection Attacks (FDIAs) in the multi-area system. Firstly, we describe the implementation of a three-area system model. Next, we assess the risk and devise several intrusion scenarios. Specifically, we inject false data into the frequency measurement and Automatic Generation Control (AGC) signals. We then develop a rule-based method to detect anomalies at the system-level. Our simulation results proves that the proposed algorithm can detect FDIAs in the system.
Jin, Z., Yu, P., Guo, S. Y., Feng, L., Zhou, F., Tao, M., Li, W., Qiu, X., Shi, L..  2020.  Cyber-Physical Risk Driven Routing Planning with Deep Reinforcement-Learning in Smart Grid Communication Networks. 2020 International Wireless Communications and Mobile Computing (IWCMC). :1278—1283.
In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.
Aigner, A., Khelil, A..  2020.  A Security Qualification Matrix to Efficiently Measure Security in Cyber-Physical Systems. 2020 32nd International Conference on Microelectronics (ICM). :1–4.

Implementations of Cyber-Physical Systems (CPS), like the Internet of Things, Smart Factories or Smart Grid gain more and more impact in their fields of application, as they extend the functionality and quality of the offered services significantly. However, the coupling of safety-critical embedded systems and services of the cyber-space domain introduce many new challenges for system engineers. Especially, the goal to achieve a high level of security throughout CPS presents a major challenge. However, it is necessary to develop and deploy secure CPS, as vulnerabilities and threats may lead to a non- or maliciously modified functionality of the CPS. This could ultimately cause harm to life of involved actors, or at least sensitive information can be leaked or lost. Therefore, it is essential that system engineers are aware of the level of security of the deployed CPS. For this purpose, security metrics and security evaluation frameworks can be utilized, as they are able to quantitatively express security, based on different measurements and rules. However, existing security scoring solutions may not be able to generate accurate security scores for CPS, as they insufficiently consider the typical CPS characteristics, like the communication of heterogeneous systems of physical- and cyber-space domain in an unpredictable manner. Therefore, we propose a security analysis framework, called Security Qualification Matrix (SQM). The SQM is capable to analyses multiple attacks on a System-of-Systems level simultaneously. With this approach, dependencies, potential side effects and the impact of mitigation concepts can quickly be identified and evaluated.

Shuncheng, L., Jiajia, X., Jin, C., Jian, C., Lin, D., Lu, W..  2020.  Research on the Calibration Influence Factors of UHF Partial Discharge Detector. 2020 5th International Conference on Smart Grid and Electrical Automation (ICSGEA). :34—41.

Ultra high frequency (UHF) partial discharge detection technology has been widely used in on-line monitoring of electrical equipment, for the influence factors of UHF signal's transfer function is complicated, the calibration of UHF method is still not realized until now. In order to study the calibration influence factors of UHF partial discharge (PD) detector, the discharge mechanism of typical PD defects is analyzed, and use a PD UHF signal simulator with multiple adjustable parameters to simulate types of PD UHF signals of electrical equipment, then performed the relative experimental research in propagation characteristics and Sensor characteristics of UHF signals. It is concluded that the calibration reliability has big differences between UHF signal energy and discharge capacity of different discharge source. The calibration curve of corona discharge and suspended discharge which can representation the severity of equipment insulation defect more accurate, and the calibration curve of internal air gap discharge and dielectric surface discharge is poorer. The distance of UHF signal energy decays to stable period become smaller with increase of frequency, and the decay of UHF signal energy is irrelevant to its frequencies when the measuring angle is changing. The frequency range of measuring UHF signal depends on effective frequency range of measurement sensor, moreover, the gain and standing-wave ratio of sensor and the energy of the received signal manifested same change trend. Therefore, in order to calibration the UHF signal, it is necessary to comprehensive consideration the specific discharge type and measuring condition. The results provide the favorable reference for a further study to build the calibration system of UHF measuring method, and to promote the effective application of UHF method in sensor characteristic fault diagnosis and insulation evaluation of electrical equipment.

Cao, S., Zou, J., Du, X., Zhang, X..  2020.  A Successive Framework: Enabling Accurate Identification and Secure Storage for Data in Smart Grid. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1–6.
Due to malicious eavesdropping, forgery as well as other risks, it is challenging to dispose and store collected power data from smart grid in secure manners. Blockchain technology has become a novel method to solve the above problems because of its de-centralization and tamper-proof characteristics. It is especially well known that data stored in blockchain cannot be changed, so it is vital to seek out perfect mechanisms to ensure that data are compliant with high quality (namely, accuracy of the power data) before being stored in blockchain. This will help avoid losses due to low-quality data modification or deletion as needed in smart grid. Thus, we apply the parallel vision theory on the identification of meter readings to realize accurate power data. A cloud-blockchain fusion model (CBFM) is proposed for the storage of accurate power data, allowing for secure conducting of flexible transactions. Only power data calculated by parallel visual system instead of image data collected originally via robot would be stored in blockchain. Hence, we define the quality assurance before data uploaded to blockchain and security guarantee after data stored in blockchain as a successive framework, which is a brand new solution to manage efficiency and security as a whole for power data and data alike in other scenes. Security analysis and performance evaluations are performed, which prove that CBFM is highly secure and efficient impressively.
Zhang, L., Shen, X., Zhang, F., Ren, M., Ge, B., Li, B..  2019.  Anomaly Detection for Power Grid Based on Time Series Model. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :188—192.

In the process of informationization and networking of smart grids, the original physical isolation was broken, potential risks increased, and the increasingly serious cyber security situation was faced. Therefore, it is critical to develop accuracy and efficient anomaly detection methods to disclose various threats. However, in the industry, mainstream security devices such as firewalls are not able to detect and resist some advanced behavior attacks. In this paper, we propose a time series anomaly detection model, which is based on the periodic extraction method of discrete Fourier transform, and determines the sequence position of each element in the period by periodic overlapping mapping, thereby accurately describe the timing relationship between each network message. The experiments demonstrate that our model can detect cyber attacks such as man-in-the-middle, malicious injection, and Dos in a highly periodic network.

Chen, Z., Bai, B., Chen, D., Chai, W..  2018.  Design of Distribution Devices for Smart Grid Based on Magnetically Tunable Nanocomposite. IEEE Transactions on Power Electronics. 33:2083–2099.
This paper designs three distribution devices for the smart grid, which are, respectively, novel transformer with dc bias restraining ability, energy-saving contactor, and controllable reactor with adjustable intrinsic magnetic state based on the magnetically tunable nanocomposite material core. First, the magnetic performance of this magnetic material was analyzed and the magnetic properties processing method was put forward. One kind of nanocomposite which is close to the semihard magnetic state with low coercivity and high remanence was attained. Nanocomposite with four magnetic properties was processed and prepared using the distribution devices design. Second, in order to adjust the magnetic state better, the magnetization and demagnetization control circuit based on the single-phase supply power of rectification and inverter for the nanocomposite magnetic performance adjustment has been designed, which can mutual transform the material's soft and hard magnetic phases. Finally, based on the nanocomposite and the control circuit, a novel power transformer, an energy-saving contactor, and a magnetically controllable reactor were manufactured for the smart grid. The maintained remanence of the nanocomposite core after the magnetization could neutralize the dc bias magnetic flux in the transformer main core without changing the transformer neutral point connection mode, could pull in the contactor movable core instead of the traditional electromagnetic-type fixed core, and could adjust the reactor core saturation degree instead of the traditional electromagnetic coil. The simulation and experimental results verify the correctness of the design, which provides reliable, intelligent, interactive, and energy-saving power equipment for the smart power grids safe operation.
Prasad, G., Huo, Y., Lampe, L., Leung, V. C. M..  2019.  Machine Learning Based Physical-Layer Intrusion Detection and Location for the Smart Grid. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1—6.
Security and privacy of smart grid communication data is crucial given the nature of the continuous bidirectional information exchange between the consumer and the utilities. Data security has conventionally been ensured using cryptographic techniques implemented at the upper layers of the network stack. However, it has been shown that security can be further enhanced using physical layer (PHY) methods. To aid and/or complement such PHY and upper layer techniques, in this paper, we propose a PHY design that can detect and locate not only an active intruder but also a passive eavesdropper in the network. Our method can either be used as a stand-alone solution or together with existing techniques to achieve improved smart grid data security. Our machine learning based solution intelligently and automatically detects and locates a possible intruder in the network by reusing power line transmission modems installed in the grid for communication purposes. Simulation results show that our cost-efficient design provides near ideal intruder detection rates and also estimates its location with a high degree of accuracy.
Roy, D. D., Shin, D..  2019.  Network Intrusion Detection in Smart Grids for Imbalanced Attack Types Using Machine Learning Models. 2019 International Conference on Information and Communication Technology Convergence (ICTC). :576—581.
Smart grid has evolved as the next generation power grid paradigm which enables the transfer of real time information between the utility company and the consumer via smart meter and advanced metering infrastructure (AMI). These information facilitate many services for both, such as automatic meter reading, demand side management, and time-of-use (TOU) pricing. However, there have been growing security and privacy concerns over smart grid systems, which are built with both smart and legacy information and operational technologies. Intrusion detection is a critical security service for smart grid systems, alerting the system operator for the presence of ongoing attacks. Hence, there has been lots of research conducted on intrusion detection in the past, especially anomaly-based intrusion detection. Problems emerge when common approaches of pattern recognition are used for imbalanced data which represent much more data instances belonging to normal behaviors than to attack ones, and these approaches cause low detection rates for minority classes. In this paper, we study various machine learning models to overcome this drawback by using CIC-IDS2018 dataset [1].
Lu, X., Guan, Z., Zhou, X., Du, X., Wu, L., Guizani, M..  2019.  A Secure and Efficient Renewable Energy Trading Scheme Based on Blockchain in Smart Grid. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :1839—1844.
Nowadays, with the diversification and decentralization of energy systems, the energy Internet makes it possible to interconnect distributed energy sources and consumers. In the energy trading market, the traditional centralized model relies entirely on trusted third parties. However, as the number of entities involved in the transactions grows and the forms of transactions diversify, the centralized model gradually exposes problems such as insufficient scalability, High energy consumption, and low processing efficiency. To address these challenges, we propose a secure and efficient energy renewable trading scheme based on blockchain. In our scheme, the electricity market trading model is divided into two levels, which can not only protect the privacy, but also achieve a green computing. In addition, in order to adapt to the relatively weak computing power of the underlying equipment in smart grid, we design a credibility-based equity proof mechanism to greatly improve the system availability. Compared with other similar distributed energy trading schemes, we prove the advantages of our scheme in terms of high operational efficiency and low computational overhead through experimental evaluations. Additionally, we conduct a detailed security analysis to demonstrate that our solution meets the security requirements.
Lardier, W., Varo, Q., Yan, J..  2019.  Quantum-Sim: An Open-Source Co-Simulation Platform for Quantum Key Distribution-Based Smart Grid Communications. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1—6.
Grid modernization efforts with the latest information and communication technologies will significantly benefit smart grids in the coming years. More optical fibre communications between consumers and the control center will promise better demand response and customer engagement, yet the increasing attack surface and man-in-the-middle (MITM) threats can result in security and privacy challenges. Among the studies for more secure smart grid communications, quantum key distribution protocols (QKD) have emerged as a promising option. To bridge the theoretical advantages of quantum communication to its practical utilization, however, comprehensive investigations have to be conducted with realistic cyber-physical smart grid structures and scenarios. To facilitate research in this direction, this paper proposes an open-source, research-oriented co-simulation platform that orchestrates cyber and power simulators under the MOSAIK framework. The proposed platform allows flexible and realistic power flow-based co-simulation of quantum communications and electrical grids, where different cyber and power topologies, QKD protocols, and attack threats can be investigated. Using quantum-based communication under MITM attacks, the paper presented detailed case studies to demonstrate how the platform enables quick setup of a lowvoltage distribution grid, implementation of different protocols and cryptosystems, as well as evaluations of both communication efficiency and security against MITM attacks. The platform has been made available online to empower researchers in the modelling of quantum-based cyber-physical systems, pilot studies on quantum communications in smart grid, as well as improved attack resilience against malicious intruders.
Romdhane, R. B., Hammami, H., Hamdi, M., Kim, T..  2019.  At the cross roads of lattice-based and homomorphic encryption to secure data aggregation in smart grid. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :1067—1072.

Various research efforts have focused on the problem of customer privacy protection in the smart grid arising from the large deployment of smart energy meters. In fact, the deployed smart meters distribute accurate profiles of home energy use, which can reflect the consumers' behaviour. This paper proposes a privacy-preserving lattice-based homomorphic aggregation scheme. In this approach, the smart household appliances perform the data aggregation while the smart meter works as relay node. Its role is to authenticate the exchanged messages between the home area network appliances and the related gateway. Security analysis show that our scheme guarantees consumer privacy and messages confidentiality and integrity in addition to its robustness against several attacks. Experimental results demonstrate the efficiency of our proposed approach in terms of communication complexity.

Song, Yufei, Yu, Zongchao, Liu, Xuan, Tian, Jianwei, CHEN, Mu.  2019.  Isolation Forest based Detection for False Data Attacks in Power Systems. 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). :4170—4174.
Power systems become a primary target of cyber attacks because of the vulnerability of the integrated communication networks. An attacker is able to manipulate the integrity of real-time data by maliciously modifying the readings of meters transmitted to the control center. Moreover, it is demonstrated that such attack can escape the bad data detection in state estimation if the topology and network information of the entire power grid is known to the attacker. In this paper, we propose an isolation forest (IF) based detection algorithm as a countermeasure against false data attack (FDA). This method requires no tedious pre-training procedure to obtain the labels of outliers. In addition, comparing with other algorithms, the IF based detection method can find the outliers quickly. The performance of the proposed detection method is verified using the simulation results on the IEEE 118-bus system.