A country's growth is gauged with reference to its power consumption and energy use, both of which are fast expanding. Energy consumption management improves energy generation and delivery. The smart grid is a tremendous improvement over the power system of the 20th century, using two-way electrical and communication exchanges to create a sophisticated, distributed, computerized energy delivery network. The fields of artificial intelligence (AI) and block chain distributed ledger technology (BDLT) are the most fascinating areas of study in the field of green energy and related power automation. An in-depth analysis of the most advanced automated planning, governance, optimization, confidentiality, and safety methods for the distribution of power and smart grid using integrated artificial intelligence and block chain is presented in this chapter.
The massive installation of smart meters on the customer side plays an important role in the collection and analysis of multi-user data. However, the current way of power grid collecting electricity data is consistent with the centralized grid control, and this way cannot cope with the new demand of multiparty data interaction and integration in the future. Blockchain technology integrates cryptography, distributed ledger technology and data sharing, which can enhance the security of power measurement data. To improve the performance of power data transmission and transaction, this paper combines blockchain smart contracts with the practicality in the field of power measurement, and proposes a smart contract based on the identification of hydrogen energy users, which analyzes the nodes containing hydrogen energy and identifies different nodes to complete the contract.
Stephen Kirkman, Steven Fulton, Jeffrey Hemmes, Christopher Garcia · 5 authors
The motivation of this research (and also one of the nation’s cyber goals) is enhancing the resilience of Industrial Control Systems (ICS)/Supervisory Control and Data Acquisition (SCADA) systems against ransomware attacks. ICS and SCADA systems run some of the most important networks in the country: our critical infrastructure (i.e., water flow, power grids, etc.). Disruption of these systems causes confusion, panic, and in some cases loss of life. We propose a SCADA architecture that uses blockchain to help protect ICS data from ransomware. We focus on the historian. In a SCADA system, the historian collects events from devices in the control network for real-time and future analysis. We choose to use Ethereum and its Proof of Stake (PoS) consensus protocol. The other goal of this research focuses on the resilience of blockchain. There is very little research in protecting the blockchain itself. By performing encryption testing on an Ethereum private network, we explore how vulnerable blockchain is and discuss potential ways to make a blockchain client more resilient.
With the proliferation of interconnected electrical systems and the rise of decentralized energy resources, the need for secure, transparent, and decentralized data exchange within smart grid systems has become imperative. This paper investigates the potential of integrating blockchain technology into smart grid systems to ensure secure data exchange while enhancing interoperability. The study establishes that blockchain's immutable ledger capabilities can be tailored to authenticate and validate the vast volume of transactions inherent in smart grid systems. Additionally, a prototype for blockchain-enhanced secure data exchange is presented, emphasizing consensus algorithms that are both energy-efficient and capable of real-time processing. The prototype demonstrates a substantial reduction in malicious data injections, unauthorized access, and other potential security breaches. Furthermore, the blockchain's decentralized nature promotes increased resilience against single-point failures, promoting reliability in smart grid data exchange. In essence, the synergy between blockchain technology and smart grid systems offers a promising avenue for creating a more secure, transparent, and interoperable energy network, pivotal for the future of distributed energy systems.
With the increasing popularization and application of the smart grid, the harm of the data silo issue in the smart grid is more and more prominent. Therefore, it is especially critical to promote data interoperability and sharing in the smart grid. Existing data-sharing schemes generally lack effective incentive mechanisms, and data holders are reluctant to share data due to privacy and security issues. Because of the above issues, a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory is proposed. Firstly, several basic assumptions about the evolutionary game model are given, and the evolutionary game payoff matrix is established. Then, we analyze the stabilization strategy of the evolutionary game based on the payoff matrix, and propose a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory according to the analysis results, aiming to encourage user participation in data sharing. We further write the above evolutionary game model into a smart contract that can be invoked by the two parties involved in data sharing. Finally, several factors affecting the sharing of data between two users are simulated, and the impact of different factors on the evolutionary stabilization strategy is discussed. The simulation results verify the positive or negative incentives of these parameters in the data-sharing game process, and several factors influencing the users’ data sharing are specifically analyzed. This dynamic incentive mechanism scheme for smart grid data sharing based on evolutionary game theory provides new insights into effective incentives for current smart grid data sharing.
The need for effective and secure energy management systems has arisen as a result of increased demand for energy, the increasing use of renewable energy sources (RES), and the introduction of decentralized power generation. Here, we explore the urgent issues and exciting possibilities posed by traditional centralized energy networks, with a special focus on three crucial dimensions: achieving supply-demand equilibrium, seamlessly integrating renewable energy sources, and maintaining participant trust and transparency. This paper suggests leveraging blockchain technology (BT) to create a decentralized network grid energy management system to address these issues. Individual participants can directly exchange energy with one another within the Decentralized Network Grid through peer-to-peer (P2P) trading. The network microgrid model’s mathematical optimization is designed to balance supply and demand, improve network microgrid stability, and optimize energy distribution. The aforementioned optimization uses information from grid constraints, usage patterns, and energy pricing to make wise decisions in real time. The concept aims to achieve energy efficiency, cost savings, and seamless integration of renewable energy sources by utilizing the capabilities of blockchain smart contract and proposed optimization algorithm.
Mazin Abed Mohammed, Abdullah Lakhan, Dilovan Asaad Zebari, Mohd Khanapi Abd Ghani · 8 authors
Industrial cyber–physical systems (ICPS) are emerging platforms for various industrial applications. For instance, remote healthcare monitoring, real-time healthcare data generation, and many other applications have been integrated into the ICPS platform. These healthcare applications encompass workflow tasks, such as processing within hospitals, laboratory tests, and insurance companies for patient payments, which necessitate a sequential flow. The external wireless, fog, and cloud services within ICPS face security issues that impact end-users’ healthcare applications. Blockchain technology offers an optimal solution for ICPS-enabled applications. However, blockchain technology for the ICPS platform is still vulnerable to cyberattacks, while microservices are essential for executing applications. This paper introduces the novel “Pattern-Proof Malware Validation” (PoPMV) algorithm designed for blockchain in ICPS. It exploits a deep learning model (LSTM) with reinforcement learning techniques to receive feedback and rewards in real-time. The primary objective is to mitigate security vulnerabilities, enhance processing speed, identify both familiar and unfamiliar attacks, and optimize the functionality of ICPS. Simulations demonstrate the superiority of the proposed approach compared to current blockchain frameworks, showcasing dynamic allocation of microservices and improved security with comprehensive attack detection by 30%.
Amir-Saeed Es’Haghi, Ebrahim Afjei, Abbas Marini, Maziar Karimi
In recent years, the mining of cryptocurrencies has raised concerns due to its high profitability and secure environment, especially in countries with low energy prices. These concerns have resulted in a range of problems, including increased network load, electricity theft, harmonic distortion in the network, and a degradation in power quality, as well as an inaccurate estimation of load behaviors. This article presents a new approach that utilizes the concept of harmonic state estimation and unique characteristics of mining loads to identify unauthorized mining farms at the distribution network levels. Since miners are based on electronic power switching devices, they are recognized as harmonic-polluting loads. Using measurements and harmonic state estimation, it becomes possible to identify the potential locations of these loads. The proposed approach was implemented in DigSILENT software and tested on an 18-bus IEEE network. The results demonstrate the effectiveness of the proposed method in identifying the locations of harmonic loads from mining operations, detecting unauthorized energy consumption.
Mouhamad Almakhour, Layth Sliman, Abed Ellatif Samhat, Boussad Ait Salem · 5 authors
Recently, the integration of Network Function Virtualization (NFV) with the blockchain has been gaining a lot of attention. This combination aims to avoid traditional NFV issues such as trust, payment, and security. Several works have been proposed to orchestrate, buy, execute, and manage the life cycle of a Virtual Network Function (VNF). Thus, they came as NFV marketplaces and orchestration platforms. In this paper, we provide a novel secure End-to-End NFV marketplace called “VNFO-DCSC”, that uses dynamic composite smart contracts. This platform provides full orchestration, management, execution, and monitoring of VNFs in a secure way. “VNFO-DCSC” is implemented following the European Telecommunications Standards Institute (ETSI) standards [1] for NFV management and it is available on GitHub.
The industrial Internet of Things (IIoT) involves the integration of Internet of Things (IoT) technologies into industrial settings. However, given the high sensitivity of the industry to the security of industrial control system networks and IIoT, the use of software-defined networking (SDN) technology can provide improved security and automation of communication processes. Despite this, the architecture of SDN can give rise to various security threats. Therefore, it is of paramount importance to consider the impact of these threats on SDN-based IIoT environments. Unlike previous research, which focused on security in IIoT and SDN architectures separately, we propose an integrated method including two components that work together seamlessly for better detecting and preventing security threats associated with SDN-based IIoT architectures. The two components consist in a convolutional neural network-based Intrusion Detection System (IDS) implemented as an SDN application and a Blockchain-based system (BS) to empower application layer and network layer security, respectively. A significant advantage of the proposed method lies in jointly minimizing the impact of attacks such as command injection and rule injection on SDN-based IIoT architecture layers. The proposed IDS exhibits superior classification accuracy in both binary and multiclass categories.
Mohammad Seyfi, Mehdi Mehdinejad, Behanm Mohammadi-Ivatloo, Jamshid Aghaei
Power systems undergoes a massive change in power delivery and consumer-side production, e.g., penetration of renewable energy resources (RES) and reshaping of consumers to form prosumers, in which the Peer-to-Peer (P2P) energy trading markets are among the most promising solutions for handling these changes. In this paper, a fully decentralized smart contract-based P2P energy token trading market for active retailers and prosumers is presented. Active retailers in this market play as a connection between the local P2P market and upstream markets, which enables the participation of small-scale prosumers in the energy and ancillary markets. They can optimize their decision-making strategy to gain the most profit from energy markets. This model can first encourage retailers to participate in the local P2P energy token market, and consequently, the utilization of renewable energy resources in the power systems is facilitated. The simulation results showed the importance of the demand response program and the effectiveness of the DR program on the independence of the local P2P energy token trading market.
The increasing reliance on cyber-physical systems (CPSs) in critical domains such as healthcare, smart grids, and intelligent transportation systems necessitates robust security measures to protect against cyber threats. Among these threats, blackhole and greyhole attacks pose significant risks to the availability and integrity of CPSs. The current detection and mitigation approaches often struggle to accurately differentiate between legitimate and malicious behavior, leading to ineffective protection. This paper introduces Gini-index and blockchain-based Blackhole/Greyhole RPL (GBG-RPL), a novel technique designed for efficient detection and mitigation of blackhole and greyhole attacks in smart health monitoring CPSs. GBG-RPL leverages the analytical prowess of the Gini index and the security advantages of blockchain technology to protect these systems against sophisticated threats. This research not only focuses on identifying anomalous activities but also proposes a resilient framework that ensures the integrity and reliability of the monitored data. GBG-RPL achieves notable improvements as compared to another state-of-the-art technique referred to as BCPS-RPL, including a 7.18% reduction in packet loss ratio, an 11.97% enhancement in residual energy utilization, and a 19.27% decrease in energy consumption. Its security features are also very effective, boasting a 10.65% improvement in attack-detection rate and an 18.88% faster average attack-detection time. GBG-RPL optimizes network management by exhibiting a 21.65% reduction in message overhead and a 28.34% decrease in end-to-end delay, thus showing its potential for enhanced reliability, efficiency, and security.
В статье рассматривается технология работы алгоритмов распределенного реестра (блокчейн) и смарт-контрактов в применении к индустрии энергетики. Показано, как преимущества технологии могут способствовать оптимизации энергетической отрасли в свете продолжающегося энергоперехода, рассмотрены примеры мировых проектов внедрения блокчейн в существующие или новые энергосистемы, обсуждаются риски и препятствия, связанные с имплементацией решений платформ блокчейн в энергетические системы. The article discusses the technology of operation of distributed ledger algorithms (blockchain) and smart contracts as applied to the energy industry. It is shown how the advantages of technology can help optimize the energy industry amid ongoing energy transition, examples of global projects for implementing blockchain into existing or new energy systems are reviewed, and risks and obstacles associated with the implementation of blockchain platform solutions in energy systems are discussed.
The paper introduces an advanced Decentralized Energy Marketplace (DEM) integrating blockchain technology and artificial intelligence to manage energy exchanges among smart homes with energy storage systems. The proposed framework uses Non-Fungible Tokens (NFTs) to represent unique energy profiles in a transparent and secure trading environment. Leveraging Federated Deep Reinforcement Learning (FDRL), the system promotes collaborative and adaptive energy management strategies, maintaining user privacy. A notable innovation is the use of smart contracts, ensuring high efficiency and integrity in energy transactions. Extensive evaluations demonstrate the system's scalability and the effectiveness of the FDRL method in optimizing energy distribution. This research significantly contributes to developing sophisticated decentralized smart grid infrastructures. Our approach broadens potential blockchain and AI applications in sustainable energy systems and addresses incentive alignment and transparency challenges in traditional energy trading mechanisms. The implementation of this paper is publicly accessible at \url{https://github.com/RasoulNik/DEM}.
Marco Gerardi, Francesca Fallucchi, Fabio Orecchini
The growing adoption of renewable energy sources and the need for more efficient and secure energy grids are revolutionizing the energy sector. Electricity monitoring becomes an issue of utmost importance, as current traditional energy meters have several problems in terms of lack of transparency, very high operational costs, and the possibility of being easily tampered with. This paper proposes a new system for electricity production metering that leverages blockchain and IoT for decentralized and secure data recording while protecting user privacy and reducing operational costs. The architecture results in improvements over the traditional energy meter. The system also contributes to the generation of big data that is reliable, traceable, error-proof, and highly resistant to cyber attacks. The architectural project outputs are a smart energy meter, a smart contract on the Ethereum blockchain, and a decentralized application to manage the information recording. The experimental prototype outcomes confirm the use of these new technologies to improve energy metering, enhancing efficiency, transparency, and traceability, with reduced costs and increased user privacy.
False data injection attacks (FDIA) pose a significant threat to the microgrids by corrupting information exchange among controller units. An effective solution to enhance the cyber-resilience is urgently needed, given the unbalanced advantages between the attacker and defender. To mitigate this issue, a proposed framework for enhancing cyber-resilience leverages the intrinsic security of blockchain technology to replace vulnerable information exchange and computation with secure transactions. Unlike the current approaches in the control field with limited cyber-resilience, this framework considers both the communication and control fields. Smart contracts (SCs) deployed on an enterprise-level HyperLedger blockchain provide distributed secondary control and self-healing functions, securing microgrid secondary control against FDIAs in a zero-trust environment. The proposed framework is validated through a four-DG microgrid system on a hardware-in-the-loop testbed. The results demonstrate that the framework provides comparable distributed control performance to conventional approaches, even in cases where the intensity of the FDIA launched exceeds the theoretical fault-tolerance of the blockchain technology.
The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT's sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, we propose an artificial intelligence (AI) and blockchain-driven secure data dissemination architecture to deal with critical infrastructure security and privacy issues. First, we reduced dimensionality using principal component analysis (PCA) and explainable AI (XAI) approaches. Furthermore, we applied different AI classifiers such as random forest (RF), decision tree (DT), support vector machine (SVM), perceptron, and Gaussian Naive Bayes (GaussianNB) that classify the data, i.e., malicious or non-malicious. Furthermore, we employ an interplanetary file system (IPFS)-driven blockchain network that offers security to the non-malicious data. In addition, to strengthen the security of AI classifiers, we analyze data poisoning attacks on the dataset that manipulate sensitive data and mislead the classifier, resulting in inaccurate results from the classifiers. To overcome this issue, we provide an anomaly detection approach that identifies malicious instances and removes the poisoned data from the dataset. The proposed architecture is evaluated using performance evaluation metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). The findings show that the RF classifier transcends other AI classifiers in terms of accuracy, i.e., 98.46%.
In recent years, the integration of advanced technologies in industrial control processes has gained significant attention, particularly in the domain of wastewater systems. One emerging technology with promising potential is Distributed Ledger Technology (DLT), which offers secure and transparent data management through blockchain-based solutions. This paper presents an in-depth analysis of the performance impact that arises when incorporating DLT-based sensor authentication in industrial control processes of wastewater systems. The study aims to evaluate the benefits and challenges associated with this integration, providing insights into the effectiveness and efficiency of DLT-based sensor authentication in ensuring data integrity and enhancing the overall control process performance.
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Cyber threats and vulnerabilities present an increasing risk to the safe and frictionless execution of business operations. Bad actors ("hackers"), including state actors, are increasingly targeting the operational technologies (OTs) and industrial control systems (ICSs) used to protect critical national infrastructure (CNI). Minimisations of cyber risk, attack surfaces, data immutability, and interoperability of IoT are some of the main challenges of today's CNI. Cyber security risk assessment is one of the basic and most important activities to identify and quantify cyber security threats and vulnerabilities. This research presents a novel i-TRACE security-by-design CNI methodology that encompasses CNI key performance indicators (KPIs) and metrics to combat the growing vicarious nature of remote, well-planned, and well-executed cyber-attacks against CNI, as recently exemplified in the current Ukraine conflict (2014-present) on both sides. The proposed methodology offers a hybrid method that specifically identifies the steps required (typically undertaken by those responsible for detecting, deterring, and disrupting cyber attacks on CNI). Furthermore, we present a novel, advanced, and resilient approach that leverages digital twins and distributed ledger technologies for our chosen i-TRACE use cases of energy management and connected sites. The key steps required to achieve the desired level of interoperability and immutability of data are identified, thereby reducing the risk of CNI-specific cyber attacks and minimising the attack vectors and surfaces. Hence, this research aims to provide an extra level of safety for CNI and OT human operatives, i.e., those tasked with and responsible for detecting, deterring, disrupting, and mitigating these cyber-attacks. Our evaluations and comparisons clearly demonstrate that i-TRACE has significant intrinsic advantages compared to existing "state-of-the-art" mechanisms.
Open access
Smart Grid Security and Resilience
Information and Cyber Security
Infrastructure Resilience and Vulnerability Analysis
Ziqiang Xu, Ahmad Salehi Shahraki, Carsten Rudolph
The smart grid optimises energy transmission efficiency and provides practical solutions for energy saving and life convenience. Along with a decentralised, transparent and fair trading model, the smart grid attracts many users to participate. In recent years, many researchers have contributed to the development of smart grids in terms of network and information security so that the security, reliability and stability of smart grid systems can be guaranteed. However, our investigation reveals various malicious behaviours during smart grid transactions and operations, such as electricity theft, erroneous data injection, and distributed denial of service (DDoS). These malicious behaviours threaten the interests of honest suppliers and consumers. While the existing literature has employed machine learning and other methods to detect and defend against malicious behaviour, these defence mechanisms do not impose any penalties on the attackers. This paper proposes a management scheme that can handle different types of malicious behaviour in the smart grid. The scheme uses a consortium blockchain combined with the best–worst multi-criteria decision method (BWM) to accurately quantify and manage malicious behaviour. Smart contracts are used to implement a penalty mechanism that applies appropriate penalties to different malicious users. Through a detailed description of the proposed algorithm, logic model and data structure, we show the principles and workflow of this scheme for dealing with malicious behaviour. We analysed the system’s security attributes and tested the system’s performance. The results indicate that the system meets the security attributes of confidentiality and integrity. The performance results are similar to the benchmark results, demonstrating the feasibility and stability of the system.
Aaron Werth, Gary Hahn, Raymond Borges Hink, Emilio C. Piesciorovsky · 5 authors
This work involves the development of a device - EmSense (“Emulated Sensor”) - that emulates a high-resolution sensor for a power grid. The device collects raw current and voltage sensor data which derive from ORNL's signature library. This library is a dataset that ORNL curates from many different sources that include power systems from various utilities. The EmSense packages the data from the library in the form of IEC 61850 Sampled Value (SV) packets and then broadcasts these SV packets on the network. In another mode, EmSense can generate artificial sinusoidal data that appears as waveforms for voltage and current signals. EmSense has an internal algorithm for determining the period of a signal based on the data so that the period can be specified as a variable in the IEC 61850 packets. The purpose of EmSense is to allow for experimentation with the Dark Net Infrastructure where a variety of power line sensors must be represented along with their typical communication traffic. The EmSense device was developed in coordination with the software for receiving and processing the packets in the Distributed Ledger Technology (DLT) framework of the DarkNet Project. This receiving software must have a methodology for dealing with information of high velocity, variety, and volume. Experimenting with EmSense facilitates the development of such software. The results showed that the DLT framework and the trust-anchoring approach managed to process a large flow of traffic even with up to six instances of EmSense device broadcasting data. This was achieved without overfilling packet queues in the memory of the actual hardware of the DLT devices or causing the Central Processing Unit (CPU) of the hardware to be overwhelmed. The DLTs were also able to store the data in a compact and useful form for later analysis and archival purposes.