The advancement and proliferation of digitalization and communication infrastructure have facilitated the rise of real-time bidding markets in smart grids. In these dynamic markets, energy distribution companies and power-generating companies interact to establish energy exchange contracts based on offered prices. However, the fluctuation in power flow resulting from contract changes within the real-time bidding market introduces a potential vulnerability that malicious attackers can exploit to launch successful stealthy attacks. To enhance the smart grid resiliency against cyber-attack in the power market bidding environment, a new barrier-function adaptive finite-time trajectory tracking control is proposed in this paper. The developed controller is utilized to actively counteract and mitigate potential cyber-attacks to ensure their rejection and prevention. The stability analysis convincingly demonstrates the rapid convergence of system states within a finite time frame, empowering the system to effectively reject cyber-attacks in real-time. Test results of an IEEE test systems, considering governor dead bound nonlinearity and communication time delay are presented and compared with those obtained from other methods to ensure and demonstrate the performance of proposed method. The Speedgoat real-time target machine, along with Simulink real-time, validates the effectiveness of the proposed method.
S. B. Goyal, Anand Singh Rajawat, Ritu Shandilya, Varun Malik
Industrial Internet of Things (IIoT) solutions have transformed industrial productivity and operations. The incorporation of Ethereum blockchain technology into IIoT creates new weaknesses, exposing industrial systems to several cyberattacks. An unique IIoT framework mitigates Ethereum-based attacks in industrial applications to solve these vulnerabilities. This system uses supervised learning and quantum classifiers to detect and fix fraudulent Ethereum transaction patterns in real time. Our methodology has lower false positive rates and higher detection accuracy than conventional methods, according to first trials. This study shows that quantum computing and machine learning (ML) can improve the security of Ethereum-enabled IIoT devices in industry.
A Multi-Controller Software-Defined Network (MC-SDN) is a revolutionary concept comprising multiple controllers and switches separated using programmable features, enhancing network availability, management, scalability, and performance. The MC-SDN is a potential choice for managing large, heterogeneous, complex industrial networks. Despite the rich operational flexibility of MC-SDN, it is imperative to protect the network deployment with proper protection against potential vulnerabilities that lead to misuse and malicious activities on the MC-SDN structure. The security holes in the MC-SDN structure significantly impact network survivability and performance efficiency. Hence, detecting MC-SDN security attacks is crucial to improving network performance. Accordingly, this work intended to design blockchain-based controller security (BCS) that exploits the advantages of immutable and distributed ledger technology among multiple controllers and securely manages the controller communications against various attacks. Thereby, it enables the controllers to maintain consistent network view and accurate flow tables among themselves and also neglects the controller failure issues. Finally, the experimental results of the proposed BCS approach demonstrated superior performance under various scenarios, such as attack detection, number of attackers, number of controllers, and number of compromised controllers, by applying different performance metrics.
Zixin Wang, Bin Cao, Yao Sun, Chenxi Liu ¡ 6 authors
Ensuring secure access to cellular networks is of paramount importance, in which system information (SI) protection plays a crucial role at the initial access stage. While the 3rd generation partnership project (3GPP) released many standardizations to enhance SI protection for preventing users from false base station (FBS) attacks, most of them are centralized solutions which are vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-enabled SI protection (BeSI), as a compatible and effective secure access scheme, is developed in this work, which aims at guaranteeing the authenticity and reliability of SI by considering the features of blockchain in immutability, traceability, and decentralization. Then, we derive a mathematical framework to justify the superiority of using blockchain in SI protection. Moreover, by resorting to a Poisson point process as the geographical model for both base stations and FBSs, we thus theoretically analyze the security gain of blockchain and understand the impact of network parameters including redundancy rate, number of confirmation blocks, and the density of base stations. Finally, numerical results are demonstrated to validate the effectiveness of BeSI.
The integration of smart contracts within water distribution networks presents a transformative approach to addressing challenges in water management. In this context, we propose a pioneering tool aimed at streamlining the design and implementation of smart contracts tailored specifically to smart water distribution networks. This tool allows stakeholders to input essential parameters such as water sources, distribution points, consumption patterns, and contractual stipulations. Through the utilization of predefined templates and adaptable contract logic, the tool automates critical processes including water allocation, usage monitoring, and penalty imposition based on predefined criteria. Furthermore, seamless integration with blockchain technology ensures the security and integrity of contract execution. By addressing scalability, compliance, and regulatory considerations, this tool represents a significant advancement in empowering stakeholders to optimize water management practices through the deployment of efficient and transparent smart contracts.
The integration of secure message authentication systems within the Industrial Internet of Things (IIoT) is paramount for safeguarding sensitive transactions. This paper introduces a Lightweight Blockchain-based Message Authentication System, utilizing k-means clustering and isolation forest machine learning techniques. With a focus on the Bitcoin Transaction Network (BTN) as a reference, this study aims to identify anomalies in IIoT transactions and achieve a high level of accuracy. The feature selection coupled with isolation forest achieved a remarkable accuracy of 92.90%. However, the trade-off between precision and recall highlights the ongoing challenge of minimizing false positives while capturing a broad spectrum of potential threats. The system successfully detected 429,713 anomalies, paving the way for deeper exploration into the characteristics of IIoT security threats. The study concludes with a discussion on the limitations and future directions, emphasizing the need for continuous refinement and adaptation to the dynamic landscape of IIoT transactions. The findings contribute to advancing the understanding of securing IIoT environments and provide a foundation for future research in enhancing anomaly detection mechanisms.
This paper explores securing Non-Fungible Tokens (NFTs) within Ethereum's Decentralized Applications (DApps), crucial in the evolving blockchain landscape. NFTs represent a paradigm shift in digital asset ownership, but their innovation introduces security challenges. We navigate Ethereum-based NFT ecosystems, highlighting vulnerabilities like smart contract exploits and authentication issues. Emphasizing the need for proactive measures, we advocate for robust smart contract design, cryptographic protocols, decentralized identity management, and user education. Our objective is to empower the Ethereum community with a comprehensive roadmap for fortifying NFT transaction security, ensuring the integrity of digital ownership in the decentralized landscape. By addressing these challenges, we aim to contribute valuable insights to the discourse on securing blockchain innovations, fostering a safer environment for NFTs within Ethereum's DApps.
Precious Kgomotso Maine, Collins Achepsah Leke, Omowunmi Mary Longe
The Gautrain railway link in the Gauteng province of South Africa is a crucial transportation facility that has faced considerable energy-related difficulties and hence a dependable power supply is essential to ensure the continuous operation of railway processes. The total number of passenger trips on the Gautrain, in a year, is two million plus. The railway system, which has always relied on grid electricity to power trains, stations, lights, security, and operations is facing increasing difficulties because of rising electricity demand and costs, and power-related outages. To address these issues, the study designed an optimised power generation system for the Gautrain railway link (GRL) using a photovoltaic (PV) system with energy storage connected to the grid to lower energy costs, promote economic growth, and satisfy its energy demand. The Advanced Interactive Multidimensional Modelling System (AIMMS) tool was used to optimise the incorporation of solar energy generation and battery energy storage into the GRL power system. This study contributes to sustainable energy development by leveraging renewable solar energy, optimising the GRL power system, and integrating blockchain technology for peer-to-peer (P2P) energy trading. A smart contract is compiled and deployed within the Remix Online IDE to apply a logic that manages energy generation, consumption, and P2P energy trading amongst prosumers. It furnishes a replicable model for the advancement of electrical energy systems within diverse transportation networks, therefore fostering the adoption of energy-efficient practices.
In the ever-changing global energy landscape, the emergence of âprosumersâ, individuals who both produce and consume energy, has blurred traditional boundaries. Driven by the growing demand for sustainability and renewable energy, prosumers play a critical role in bridging the gap between energy production and consumption. They can generate their own energy through decentralized sources like solar panels and wind turbines, and sell excess energy back to the grid. However, tracking carbon emissions and pricing strategies for prosumers pose challenges. To address this, we developed an innovative blockchain-driven peer-to-peer (P2P) trading platform for carbon allowances. This platform empowers prosumers to influence pricing and promotes a more equitable distribution of energy. The P2P platform leverages blockchain technology, a decentralized digital ledger, to provide transparency and security in carbon emission tracking and energy transactions. By eliminating intermediaries, blockchain ensures the accuracy of data and creates a tamper-proof record of energy production and consumption. This study employed a modified IEEE 37-bus test system to evaluate the efficacy of the proposed blockchain-based trading framework. The IEEE 37-bus system is a well-established benchmark for power system analysis, comprising 37 nodes, 13 generators, and 37 transmission lines. By leveraging this test system, this study demonstrated the frameworkâs ability to optimize energy consumption patterns and mitigate carbon emissions, highlighting the transformative potential of blockchain technology in the energy sector. The proposed P2P trading platform offers several benefits for prosumers: (1) Transparency: The blockchain-based platform provides a transparent record of all energy transactions, ensuring that prosumers are compensated fairly for the energy they produce. (2) Security: Blockchain technology makes it impossible to tamper with or counterfeit carbon allowances, ensuring the integrity of the trading system. (3) Efficiency: The P2P trading platform eliminates the need for intermediaries, reducing the cost and complexity of energy transactions. (4) Empowerment: The platform gives prosumers a greater say in how their energy is priced and distributed, promoting a more equitable energy system.
Marisol GarcĂaâValls, Alejandro M. Chirivella-Ciruelos
Non-functional requirements related to safety, security, and timeliness have made cyberâphysical systems (CPS) initially reluctant to their integration with blockchain technology. Despite the multiple advantages of blockchain like improved data security and traceability, the main reasons that have slowed down its adoption in CPS still remain. Examples of these are the inherent overhead of accessing the distributed ledger and the security incidents that a number of blockchain networks have suffered since its inception. This paper presents VelogCPS, a novel middleware that guarantees that logic and data managed by blockchain networks of cyberâphysical systems is verified and generated by a legitimate source. Thus, VelogCPS avoids a kind of security incidents that impact the authenticity and integrity of the logic and data managed in blockchain networks. By authenticity we refer to provenance authenticity of the involved smart contracts, i.e., the perfect matching between the source-code and a corresponding advertised smart-contract logic. This middleware ensures that the entities that participate to a CPS use solely authentic logic. For this, our approach leverages block verification services and enforces them through the operation workflow. As a result, the middleware guarantees that the CPS participants use and share authentic logic. Our approach is validated by providing an implementation on a real blockchain network, employing actual smart contract verifier logic, and analysing the temporal behavior of the overall system operations; this ensures its utility for CPS and IoT.
Open access
Blockchain Technology Applications and Security
Smart Grid Security and Resilience
Physical Unclonable Functions (PUFs) and Hardware Security
(Distributed) Denial-of-Service (DoS/DDoS) attacks are among the most dangerous cybersecurity threats to computer networks. Lately, blockchain and artificial intelligence (AI) cyberdefense applications have successfully been implemented to identify attack patterns. This paper proposes a novel collaborative, blockchain-based multi-agent reinforcement learning (RL) cyberdefense method using smart contracts. Initial numerical experiments have shown that the agents quickly learn to predict attacks, which can lead to mitigating network-wide service disruptions.
Virtual Power Plant (VPP), as a technology for the aggregated management of distributed resources, has received extensive research and application. Concurrently, as the process of power market reform advances, the collaborative operation of multiple VPPs has become a crucial research focus. Against this backdrop, this paper proposes a decentralized collaborative governance model for multiple virtual power plants based on the blockchain Delegated Proof of Stake (DPoS) consensus mechanism. Considering the dual characteristics of source and load in VPPs, a singular VPP scheduling model is introduced. The paper outlines the process of the blockchain DPoS consensus mechanism and introduces a decentralized iterative pricing mechanism for multiple virtual power plant systems. This mechanism, combined with the DPoS consensus mechanism, utilizes witness nodes as multi-centralized entities to safeguard against malicious attacks and single-point failures. Finally, case studies validate the effectiveness and rationality of the proposed model.
The seamless integration of cryptocurrencies and blockchain technology in various sectors has revolutionized financial transactions. While cryptocurrencies serve as a convenient mode of payment, they have also opened avenues promoting fraudulent schemes such as Ponzi schemes, HYIPs, or money laundering activities leading to substantial financial losses. Traditional ways of anomaly detection, such as heuristic and signature-based approaches, have proven inadequate in addressing the intricacies of burgeoning fraud patterns. This paper explores the application of ensemble learning for anomaly detection in Bitcoin transactions by combining various ML techniques such as Isolation Forest, One-class SVM, and DBSCAN within a stacking framework. The proposed model harnesses the complementary strengths of each algorithm to achieve a nearly $98 \%$ accuracy rate in anomaly detection, thereby addressing the shortcomings of existing techniques. The study utilizes hyperparameter tuning techniques to enhance the effectiveness of the ensemble model and create a resilient model for detecting fraud and security threats in cryptocurrency transactions. Leveraging the cryptographic foundations of blockchain technology, the proposed method aims to create a more secure and reliable system for detecting threats and maintaining the integrity of Bitcoin transactions.
Recent years have witnessed a significant dispersion of renewable energy and the emergence of blockchain-enabled transactive energy systems. These systems facilitate direct energy trading among participants, cutting transmission losses, improving energy efficiency, and fostering renewable energy adoption. However, developing such a system is usually challenging and time-consuming due to the diversity of energy markets. The lack of a market-agnostic design hampers the widespread adoption of blockchain-based peer-to-peer energy trading globally. In this paper, we propose and develop a novel unified blockchain-based peer-to-peer energy trading framework, called BPET. This framework incorporates microservices and blockchain as the infrastructures and adopts a highly modular smart contract design so that developers can easily extend it by plugging in localized energy market rules and rapidly developing a customized blockchain-based peer-to-peer energy trading system. Additionally, we have developed the price formation mechanisms, e.g., the system marginal price calculation algorithm and the pool price calculation algorithm, to demonstrate the extensibility of the BPET framework. To validate the proposed solution, we have conducted a comprehensive case study using real trading data from the Alberta Electric System Operator. The experimental results confirm the systemâs capability of processing energy trading transactions efficiently and effectively within the Alberta electricity wholesale market.
The world's need for energy is rising due to factors like population growth, economic expansion, and technological breakthroughs. However, there are major consequences when gas and coal are burnt to meet this surge in energy needs. Although these fossil fuels are still essential for meeting energy demands, their combustion releases a large amount of carbon dioxide and other pollutants into the atmosphere. This significantly jeopardizes community health in addition to exacerbating climate change, thus it is essential need to move swiftly to incorporate renewable energy sources by employing advanced information and communication technologies. However, this change brings up several security issues emphasizing the need for innovative cyber threats detection and prevention solutions. Consequently, this study presents bigdata sets obtained from the solar and wind powered distributed energy systems through the blockchain-based energy networks in the smart grid (SG). A hybrid machine learning (HML) model that combines both the Deep Learning (DL) and Long-Short-Term-Memory (LSTM) models characteristics is developed and applied to identify the unique patterns of Denial of Service (DoS) and Distributed Denial of Service (DDoS) cyberattacks in the power generation, transmission, and distribution processes. The presented big datasets are essential and significantly helps in identifying and classifying cyberattacks, leading to predicting the accurate energy systems behavior in the SG.
The integration of blockchain technology into smart grids represents a transformative approach to addressing key challenges in modern energy systems, particularly focusing on enhancing security, efficiency, and resilience. Smart grids, enabled by advanced communication and control technologies, aim to revolutionize the traditional energy grid by facilitating real-time monitoring, automated control, and optimized energy distribution. However, these systems are susceptible to various vulnerabilities, including centralized control points, data manipulation, and cyberattacks, which threaten the security and reliability of energy supply. Blockchain technology offers a decentralized, immutable, and transparent framework that can significantly enhance the security, efficiency, and resilience of smart grids. By leveraging blockchain's cryptographic mechanisms and distributed ledger technology, smart grids can ensure secure authentication, authorization, and transaction management, mitigating risks associated with unauthorized access and data manipulation. Moreover, blockchain enables peer-to-peer energy trading, allowing consumers to directly exchange energy with each other, thereby enhancing efficiency by optimizing energy utilization and reducing transmission losses. Furthermore, blockchain's decentralized nature decentralizes control and data management functions, enhancing the resilience of smart grids against single points of failure and malicious attacks. This work presents an overview of the integration of blockchain technology in smart grids and its potential to enhance security, efficiency, and resilience in modern energy systems. Through a comprehensive exploration of blockchain-based solutions, this paper aims to provide insights into the transformative impact of blockchain technology on the future of energy grids and pave the way for a more secure, efficient, and resilient energy infrastructure.
Peer-to-peer (P2P) renewable energy trading, facilitated by designing P2P market smart contracts on blockchain servers, is a promising approach to increase investments in cleaner energy generation. To enhance trading efficiency and pricing fairness, as the major challenges of P2P market designs, this study introduces two new market mechanisms, Hybrid Auction Coalition (HAC), and innovative coalition business model (ICBM), respectively. The performance of these market mechanisms is contrasted with existing mechanisms from the literature with respect to electricity bills, market efficiency, fairness, and blockchain feasibility using a comprehensive list of indicators including costs and profits, fairness, market efficiency, and technical viability. Compared to traditional billing, ICBM and HAC increase sellersâ profit by 88% and 66% respectively, while both impose 13% more costs on buyers. ICBM and HAC also set the fairest prices compared to the existing markets. ICBM is shown to improve blockchain feasibility and market efficiency due to lighter on-chain calculations, and absolute clearing mechanisms. The results also demonstrate that HAC outperforms standalone auctions in every aspect which endorses the hybridization benefits. ⢠Novel and efficient P2P Models, and blockchain-based mechanisms for P2P renewable energy trading. ⢠Enhanced fairness concerns in P2P trading through a novel pricing mechanism. ⢠Improved P2P trading efficiency & potentially increased local grid self-sufficiency. ⢠Technical comparison of state-of-the-art blockchain-based P2P trading markets.
Abstract As an important manifestation of the current development and transformation of the worldâs power and energy industries, the virtual power plant is an important foundation for optimizing the layout of energy resources. However, since there are many open channels in the virtual power plant, adversaries can implement eavesdropping, replay, impersonation, forgery, and other attacks to access the virtual power plant, and even publish false data in the virtual power plant to disrupt the operation of the virtual power plant. In addition, it is easy for an adversary to deduce key information such as the layout of virtual power plant equipment through the identity of the device. In this context, to ensure the security and privacy of devices when accessing the platform, in this paper, we propose an efficient authentication protocol based on the elliptic curve cryptography and zero-knowledge proof, which requires only two information exchanges. Security analysis shows that the proposed protocol can meet security features such as mutual authentication, key agreement, perfect forward secrecy, and device anonymity. Performance analysis indicates that the proposed protocol achieves a reasonable balance between computational and signaling overhead, and it is more suitable for achieving efficient device authentication and privacy protection in virtual power plants.
: In the realm of data management, data preservation stands as a critical undertaking aimed at preserving and upholding the integrity of data. Regardless of whether it concerns personal or enterprise data, the detrimental effects of malicious alterations implemented by attackers cannot be overlooked. Particularly in conventional industrial control environments, the prevalent practice involves the transmission of data from sensors to databases for storage purposes. However, it is essential to recognize that this process exposes the data to various vulnerabilities. Thus, to ensure the long-term security and reliability of the data, it becomes imperative to implement robust data preservation strategies within these industrial control systems. However, the reliance of these databases on physical hard disks introduces inherent vulnerabilities, including the potential for data loss due to disk damage or targeted malicious attacks. Consequently, it becomes imperative to prioritize the implementation of robust data preservation measures. These measures are crucial in mitigating the risk of disruptions and protecting critical data from compromise. By establishing effective data backup systems, employing advanced security protocols, and implementing proactive monitoring mechanisms, organizations can bolster their data preservation capabilities and safeguard against potential threats to data integrity and availability. As a result, many enterprises opt to store their data with third-party providers to ensure data integrity. However, this approach carries inherent risks. If the third-party service experiences an attack or if the data is tampered with, it becomes challenging to verify the integrity of the data. To address these concerns and ensure data preservation within the context of the Internet of Things (IoT), a growing number of individuals are integrating IoT with Distributed Ledger Technology (DLT). By leveraging DLT, the integrity of data can be ensured, reducing reliance on centralized third-party storage and enhancing security in the IoT ecosystem. In this article, IOTA is the DLT, which employs Directed Acyclic Graph (DAG) to store transaction information. Compared to Ethereum or other blockchain technologies, IOTA offers notable advantages in terms of transaction verification speed, making it highly suitable for real-time IoT environments. However, the conventional transmission path from sensors to IOTA nodes entails a complex route, involving multiple hardware devices before reaching the intended destination. This complexity poses challenges in ensuring data integrity during transmission and introduces vulnerabilities such as man-in-the-middle attacks or SQL injection attacks. To address these issues, we propose a method to streamline the transmission path between sensors and IOTA, specifically tailored for industrial fields with numerous IoT devices. Our approach involves preprocessing the data stored on the server using our method before uploading, ensuring data confidentiality, and leveraging IOTA to guarantee data integrity. To achieve the shortest path between IoT and DLT nodes, it becomes necessary to establish IOTA nodes on lower-level devices, such as Raspberry Pi or IoT controllers. By simplifying the transmission path, we can reduce the potential for tampering and enhance overall data security. Implementing our proposed method enables the assurance of data confidentiality and integrity during both transmission and storage on the server, strengthening the trustworthiness of the IoT, and IOTA integration.