Smart Grid Security Research Articles

Security and Privacy Challenges in the Deployment of Cyber-Physical Systems in Smart City Applications: State-of-Art Work

The concept “Smart City” is realized by incorporating advanced sensing and computational technologies in various processes, services, and infrastructure. Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the key role players in the major development of smart city infrastructure. Further, the CPS stationing has improved the quality of various services like healthcare, transportation, environmental, etc., provided in a typical smart city ecosystem. But, the embodiment of CPS has also increased the surface area for cyber-attacks. In this paper, the various security and privacy issues at the different levels in the architecture of a smart city are discussed. The domain-centric security issues evolved with the deployment of CPS in transportation, healthcare, smart grids, and smart homes are also explored. Scopus publications for the years 2016–2020 are considered for the bibliometric analysis in security. It has been observed that a major number of security solutions have been proposed in the domain of smart homes during these years, whereas much scope is still there to address security and privacy challenges for CPS equipped smart grids. But over time, the security of smart grids is also becoming an area of interest for researchers. The article is concluded with the research gaps and future scope to cater secure smart city services.

Smart Grid Security and Privacy: From Conventional to Machine Learning Issues (Threats and Countermeasures)

Smart Grid (SG) is the revolutionised power network characterised by a bidirectional flow of energy and information between customers and suppliers. The integration of power networks with information and communication technologies enables pervasive control, automation and connectivity from the energy generation power plants to the consumption level. However, the development of wireless communications, the increased level of autonomy, and the growing sofwarisation and virtualisation trends have expanded the attack susceptibility and threat surface of SGs. Besides, with the real-time information flow, and online energy consumption controlling systems, customers’ privacy and preserving their confidential data in SG is critical to be addressed. In order to prevent potential attacks and vulnerabilities in evolving power networks, the need for additional studying security and privacy mechanisms is reinforced. In addition, recently, there has been an ever-increasing use of machine intelligence and Machine Learning (ML) algorithms in different components of SG. ML models are currently the mainstream for attack detection and threat analysis. However, despite these algorithms’ high accuracy and reliability, ML systems are also vulnerable to a group of malicious activities called adversarial ML (AML) attacks. Throughout this paper, we survey and discuss new findings and developments in existing security issues and privacy breaches associated with the SG and the introduction of novel threats embedded within power systems due to the development of ML-based applications. Our survey builds multiple taxonomies and tables to express the relationships of various variables in the field. Our final section identifies the implications of emerging technologies, future communication systems, and advanced industries on the security and privacy issues of SG.

Smart Grid Security by Embedding S-Box Advanced Encryption Standard

Supervisory control and data acquisition (SCADA) systems continuously monitor the real-time processes in the smart grid. The system software, which is based on a human-machine interface (HMI), makes intelligent decisions to assist the system operator and perform normal grid management activities. The management of SCADA networks and monitoring without proper security is a major concern, as many grids and plant networks still lack necessary monitoring and detection systems, making them vulnerable to attack. SCADA networks exploit physical weaknesses as well as cyber-attacks. Researchers have developed a monitoring system based on a field-programmable gate array (FPGA) and a microcontroller that allows for faster timing and system response. The cryptography-based substitution box (S-box) Advanced Encryption Standard (AES) and decryption method were proposed in the research paper, which may be merged with smart grid communication hardware and grid distribution as an integrated chip, synthesized on FPGA-Virtex-5 hardware. The method creates a platform for grid security by implementing a cryptographic-based security algorithm in hardware chips. The algorithm was designed using VHDL coding in Xilinx ISE 14.7 software and tested with various grid data sizes and key. The research represents a significant effort on the part of the businesses looking for solutions to secure the smart grid and plants by incorporating secure hardware.

Industrial Automation Information Analogy for Smart Grid Security

Industrial automation or assembly automation is a strictly monitored environment, in which changes occur at a good speed. There are many types of entities in the focusing environment, and the data generated by these devices is huge. In addition, because the robustness is achieved by sensing redundant data, the data becomes larger. The data generating device, whether it is a sensing device or a physical device, streams the data to a higher-level deception device for calculation, so that it can be driven and configured according to the updated conditions. With the emergence of the Industry 4.0 concept that includes a variety of automation technologies, various data is generated through numerous devices. Therefore, the data generated for industrial automation requires unique Information Architecture (IA). IA should be able to satisfy hard real-time constraints to spontaneously change the environment and the instantaneous configuration of all participants. To understand its applicability, we used an example smart grid analogy. The smart grid system needs an IA to fulfill the communication requirements to report the hard real-time changes in the power immediately following the system. In addition, in a smart grid system, it needs to report changes on either side of the system, i.e., consumers and suppliers configure and reconfigure the system according to the changes. In this article, we propose an analogy of a physical phenomenon. A point charge is used as a data generating device, the streamline of electric flux is used as a data flow, and the charge distribution on a closed surface is used as a configuration. Finally, the intensity changes are used in the physical process, e.g., the smart grid. This analogy is explained by metaphors, and the structural mapping framework is used for its theoretical proof. The proposed analogy provides a theoretical basis for the development of such information architectures that can represent data flows, definition changes (deterministic and non-deterministic), events, and instantaneous configuration definitions of entities in the system. The proposed analogy provides a mechanism to perform calculations during communication, using a simple concept on the closed surface to integrate two-layer cyber-physical systems (computation, communication, and physical process). The proposed analogy is a good candidate for implementation in smart grid security.

Authentication model and trust management for secured internet of things enabled smart grid using blockchain framework

Flexible power trading is an important capability brought by smart grid to traditional electric distribution networks. Flexible power trading necessitates trust among smart grid entities, security and integrity for the transaction among entities for reliable power trading. In this work, a novel blockchain framework over internet of things enabled smart grid is proposed to facilitate secure and reliable power trading. Towards this aim, a cooperative trust-based reputation score model is proposed to provide reputation score to the entities. The communications between entities involved in power trading are authenticated using a three-party mutual authentication model. The transactions between the entities are integrity verified and secured in a blockchain framework. Use of blockchain provides an immutable storage for transactions and ensures consensus across all the entities involved in power trading, so that no entity can deny the participation in trading.

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid’s safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.

A Privacy-Aware Reconfigurable Authenticated Key Exchange Scheme for Secure Communication in Smart Grids

The smart metering infrastructure plays an important role in smart grid environments. Such metering networks need to be protected against cyber attacks by using authenticated key exchange protocols, and many relevant schemes have been presented by researchers. In addition, in order to protect against the energy theft problem, it is also important to consider physical security of the smart meter. Recently, PUFs (physical uncloneable functions) have gained popularity as a primitive against physical attacks. In 2019, we proposed the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> PUF-based authentication scheme for secure smart grid communication with resilience against physical attacks on smart meters. However, recent studies have shown that PUFs are susceptible to modeling attacks. To address this issue, this paper proposes a reconfigurable authenticated key exchange scheme for secure communication in smart grids by using the concept of reconfigurable PUFs. In addition to security, the efficiency evaluation demonstrates that our new scheme has advantages in both the computation and communication costs as compared to the state-of-the-art protocols.

An Efficient and Cloud Based Architecture for Smart Grid Security

An Efficient and Cloud Based Architecture for Smart Grid Security

Investigation of Cyber-Attack Methods and Measures in Smart Grids

Smart grids have been developing rapidly with the development of technologies in recent years. In the field of critical infrastructures such as natural gas, electricity, water, and energy systems, which are among the smart grids, its use has been increasing in Turkey and all over the world in recent years. With the increase in the use of smart grids, security problems have also gained importance. Cybersecurity attacks against these networks are increasing every year. In this research study, smart cities, smart networks, and the most common cybersecurity attack methods against these systems were investigated. The studies on security in smart grids in the last 10 years have been examined and presented in a table. As a result of these researches, cyber-attacks that are experienced and likely to occur in smart networks were determined. The 20 most used cyber-attack methods were analyzed. In addition, the measures that can be taken against cyber-attacks are analyzed in detail. In addition, in this article, studies on security problems in smart grids are examined and evaluated.

Improving smart grid security through 5G enabled IoT and edge computing

AbstractThis article investigates and analyzes the security aspects of 5G specifications from the perspective of IoT‐based smart grids. As the smart grid requires high‐speed and reliable communication to enable real‐time grid monitoring via Internet of Things (IoT) devices, 5G can be considered a catalyst to transform the current power grid infrastructure into a smart grid. Thus, an understanding of what 5G can bring in terms of cyber security in IoT‐based smart grids is important for design decisions and future risk analysis efforts. In this article, we explore a smart grid use case on automatic voltage control—a use case utilizing 5G as a wireless communication infrastructure with edge support. We identify the benefits 5G brings to several security aspects, and show how 5G security techniques are applicable to the smart grid, thus providing a foundation for future security analysis of 5G enabled smart grid systems. Future research should extend this work to additional smart grid use cases.

Blockchain based Secure Energy Trading Mechanism for Smart Grid

Increasing trend of renewable energy promoting business opportunity in the field of energy market. This enables prosumers to invest reliably in the field of energy market. Due to exponential decrease in conventional sources the system needs to be hybrid by including Distributed Generations (DGs). Current research is majorly focused on the security of smart grids through blockchain. The practical management of prosumers is not properly considered yet. In this paper, we proposed an efficient algorithm to generate Magnitude of Energy Share (MES) through smart contract for individual prosumer to tackle surplus energy generation situation. A practical approach to implement private blockchain in smart meters is also covered in this research. In this research cluster communication architecture is developed to enhance performance of DGs and reduce burden on the existing system. A novel concept of parent and neighboring nodes is developed to promote double authentication. To check the performance of proposed system different scenarios are considered and the response of our system to individual scenario is deeply covered. Also, the efficiency of current technologies and proposed system are evaluated on the basis of considered cases.

Man‐in‐the‐middle attacks and defence in a power system cyber‐physical testbed

Man-in-The-Middle (MiTM) attacks present numerous threats to a smart grid. In a MiTM attack, an intruder embeds itself within a conversation between two devices to either eavesdrop or impersonate one of the devices, making it appear to be a normal exchange of information. Thus, the intruder can perform false data injection (FDI) and false command injection (FCI) attacks that can compromise power system operations, such as state estimation, economic dispatch, and automatic generation control (AGC). Very few researchers have focused on MiTM methods that are difficult to detect within a smart grid. To address this, we are designing and implementing multi-stage MiTM intrusions in an emulation-based cyber-physical power system testbed against a large-scale synthetic grid model to demonstrate how such attacks can cause physical contingencies such as misguided operation and false measurements. MiTM intrusions create FCI, FDI, and replay attacks in this synthetic power grid. This work enables stakeholders to defend against these stealthy attacks, and we present detection mechanisms that are developed using multiple alerts from intrusion detection systems and network monitoring tools. Our contribution will enable other smart grid security researchers and industry to develop further detection mechanisms for inconspicuous MiTM attacks.

Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis

With the continuous development of smart grids, communication networks carry more and more power services, and at the same time, they are also facing more and more security issues. For example, some malicious software usually uses encryption technology or tunnel technology to bypass firewalls, intrusion detection systems, etc., thereby posing a serious threat to the information security of smart grids. At present, the classification of network traffic mainly depends on the correct extraction of network protocol characteristics. However, the process of extracting network features by some traditional methods is time-consuming and overly dependent on experience. In order to solve the problem of accurate classification of power network traffic, this paper proposes a method of convolutional neural network based on genetic algorithm optimization (GACNN) and data statistical analysis. This method can simultaneously extract the time characteristics between different packet groups and the spatial characteristics in the same packet group. Therefore, it greatly saves manpower and gets rid of the dependence on experience value. The proposed method has been tested and verified on the UNSW-NB15 dataset and the real dataset collected by the power company. The results show that the proposed method can correctly classify abnormal network flows and is much better than traditional machine learning methods. In large-scale real network flow scenarios, the detection rate of the proposed method exceeds 97%, while the traditional method is generally less than 90%.

Detection of false data cyber-attacks for the assessment of security in smart grid using deep learning

Smart Grid uses electricity and information flows to set up a highly developed, fully automated, and distributed electricity grid system. To identify the reliability of work and availability, cyber attacks detection in the smart grids play a significant role. This paper highlights the integrity of false data cyber-attacks in the physical layers of smart grids. As the first contribution, the Proposed True Data Integrity provides an attack exposure metric through an Agent-Based Model. Next, the research focuses on the decentralization of Data Integrity Security in the system with an Agent-based approach. Finally, the productivity and efficiency of the developed modeling techniques are experimentally evaluated and compared with the existing state-of-the-art supervised deep-learning models. The obtained results of the studies have shown the improved false data detection accuracy of 98.19% through replay cyber-attacks using the Artificial Feed-forward Network. Based on the research findings, deep neural network can be used to assess cyber data in smart grids to detect malware incidents and attacks.

Smart grid and nuclear power plant security by integrating cryptographic hardware chip

Present electric grids are advanced to integrate smart grids, distributed resources, high-speed sensing and control, and other advanced metering technologies. Cybersecurity is one of the challenges of the smart grid and nuclear plant digital system. It affects the advanced metering infrastructure (AMI), for grid data communication and controls the information in real-time. The research article is emphasized solving the nuclear and smart grid hardware security issues with the integration of field programmable gate array (FPGA), and implementing the latest Time Authenticated Cryptographic Identity Transmission (TACIT) cryptographic algorithm in the chip. The cryptographic-based encryption and decryption approach can be used for a smart grid distribution system embedding with FPGA hardware. The chip design is carried in Xilinx ISE 14.7 and synthesized on Virtex-5 FPGA hardware. The state of the art of work is that the algorithm is implemented on FPGA hardware that provides the scalable design with different key sizes, and its integration enhances the grid hardware security and switching. It has been reported by similar state-of-the-art approaches, that the algorithm was limited in software, not implemented in a hardware chip. The main finding of the research work is that the design predicts the utilization of hardware parameters such as slices, LUTs, flip-flops, memory, input/output blocks, and timing information for Virtex-5 FPGA synthesis before the chip fabrication. The information is extracted for 8-bit to 128-bit key and grid data with initial parameters. TACIT security chip supports 400 MHz frequency for 128-bit key. The research work is an effort to provide the solution for the industries working towards embedded hardware security for the smart grid, power plants, and nuclear applications.

A cybersecurity user authentication approach for securing smart grid communications

The smart grid provides technology that offers power solutions by integrating wireless access, renewable energy resources, smart meters, and smart appliances. Energy harvesting and distribution is automated through remotely operated commands using information and communication technology. Common issues in integrating power grid with technology include security threat that reduces the expected assimilation performance. In the present work, a cybersecurity-assisted authentication method for smart grids is introduced to overcome false data flow. This method pre-estimates the energy requirement of the meters, depending on previously acquired information. Authentication-dependent security is provided according to the pre-estimated energy requirement and distribution manner. Variations in smart grid data for energy allocation are monitored based on network and end-user consumption until the users' current connection time. This method provides individual authentication for power-sharing and user verification, thereby improving false data's detection rate. Results show that the proposed method required less detection time (4.67 s) without increasing the overload for end-users.

Voltage sag state estimator based on compressive sensing in distribution systems

This paper presents a new formulation for voltage sag state estimation based on compressive sensing (CS) theory. In a modern context, voltage sags can deteriorate the reliability, stability, and security of current networks and future smart grids. Several studies have focused on voltage sag estimation (VSE), but prefault currents, nonzero fault impedances, and unbalances are issues that can lead to inaccurate results. To overcome these limitations, we proposed a novel ℓ1-based voltage sag state estimator (ℓ1-VSSE). Only voltage measurements and a limited number of power quality meters with synchronization capabilities are considered. Sensing matrices of residual voltages per unit and a solution of a ℓ1-regularized least square problem using convex optimization are the innovative characteristics of the proposed estimator. ℓ1-VSSE efficiency has been validated by using three unbalanced distribution test systems: IEEE 34-bus, IEEE 123-bus, and EPRI-Ckt7. The results confirm the estimator’s robustness to the different faults, voltage regulation, and loading profiles.

S-DPS: An SDN-Based DDoS Protection System for Smart Grids

Information Communication Technology (ICT) environment in traditional power grids makes detection and mitigation of DDoS attacks more challenging. Existing security technologies, besides their efficiency, are not adequate to cater to DDoS security in Smart Grids (SGs) due to highly distributed and dynamic network environments. Recently, emerging Software Defined Networking- (SDN-) based approaches are proposed by researchers for SG’s DDoS protection; however, they are only able to protect against flooding attacks and are dependent on static thresholds. The proposed approach, i.e., Software Defined Networking-based DDoS Protection System (S-DPS), is efficiently addressing these issues by employing light-weight Tsallis entropy-based defense mechanisms using SDN environment. It provides early detection mechanism with mitigation of anomaly in real time. The approach offers the best deployment location of defense mechanism due to the centralized control of network. Moreover, the employment of a dynamic threshold mechanism is making detection process adaptive to the changing network conditions. S-DPS has demonstrated its effectiveness and efficiency in terms of Detection Rate (DR) and minimal CPU/RAM utilization, considering DDoS protection focusing smurf attacks, socket stress attacks, and SYN flood attacks.

Cyber Hygiene Maturity Assessment Framework for Smart Grid Scenarios

Cyber hygiene is a relatively new paradigm premised on the idea that organizations and stakeholders are able to achieve additional robustness and overall cybersecurity strength by implementing and following sound security practices. It is a preventive approach entailing high organizational culture and education for information cybersecurity to enhance resilience and protect sensitive data. In an attempt to achieve high resilience of Smart Grids against negative impacts caused by different types of common, predictable but also uncommon, unexpected, and uncertain threats and keep entities safe, the Secure and PrivatE smArt gRid (SPEAR) Horizon 2020 project has created an organization-wide cyber hygiene policy and developed a Cyber Hygiene Maturity assessment Framework (CHMF). This article presents the assessment framework for evaluating Cyber Hygiene Level (CHL) in relation to the Smart Grids. Complementary to the SPEAR Cyber Hygiene Maturity Model (CHMM), we propose a self-assessment methodology based on a questionnaire for Smart Grid cyber hygiene practices evaluation. The result of the assessment can be used as a cyber-health check to define countermeasures and to reapprove cyber hygiene rules and security standards and specifications adopted by the Smart Grid operator organization. The proposed methodology is one example of a resilient approach to cybersecurity. It can be applied for the assessment of the CHL of Smart Grids operating organizations with respect to a number of recommended good practices in cyber hygiene.

An Optimized and Decentralized Energy Provision System for Smart Cities

Energy efficiency and data security of smart grids are one of the major concerns in the context of implementing modern approaches in smart cities. For the intelligent management of energy systems, wireless sensor networks and advanced metering infrastructures have played an essential role in the transformation of traditional cities into smart communities. In this paper, a smart city energy model is proposed in which prosumer communities were built by interconnecting energy self-sufficient households to generate, consume and share clean energy on a decentralized trading platform by integrating blockchain technology with a smart microgrid. The efficiency and stability of the grid network were improved by using several wireless sensor nodes that manage a massive amount of data in the network. However, long communication distances between sensor nodes and the base station can greatly consume the energy of sensors and decrease the network lifespan. Therefore, bio-inspired algorithm approaches were proposed to improve routing by obtaining the shortest path for traversing the entire network and increasing the system performance in terms of the efficient selection of cluster heads, reduced energy consumption, and extended network lifetime. This was carried out by studying the properties and mechanisms of biological systems and applying them in the communication systems in order to obtain the best results for a specific problem. In this comprehensive model, particle swarm optimization and a genetic algorithm are used to search for the optimal solution in any problem space in less processing time.