Anand Srivatsa, Ananthapadmanabha Thammaiah, M. V. Likith Kumar, D Rajeshwari · 5 authors
Recent advances in intelligent systems have significantly improved power management, load distribution, and resource management capabilities, far beyond past constraints. Despite these gains, the development of internet-connected technology has brought various vulnerabilities, leading to negative results. The integration of intelligent technology has unintentionally offered chances for hackers to enter networks and modify data sent to central systems for analysis. One of the most serious risks is the false data injection attack (FDIA), which may drastically impair analytical outcomes. Previous research has shown that standard approaches for recovering data affected by FDIA are unreliable and inefficient. This paper investigates the use of the proof of stake (PoS) consensus method in this framework improves data integrity and makes it easier to identify illegal changes. Participating nodes may reject or change block transactions, ensuring the ledger's correctness. Our results show that the PoS consensus method is exceptionally successful in creating and adding transactions to the blockchain. Furthermore, the PoS mechanism's simplicity in block formation enhances both time and energy efficiency, resulting in considerable benefits in operational performance.
André Augusto, Rafael Belchior, Jonas Pfannschmidt, André Vasconcelos · 5 authors
Cross-chain bridges are a type of middleware for blockchain interoperability that supports the transfer of assets and data across blockchains. However, several of these bridges have vulnerabilities that have caused 3.2 billion dollars in losses since May 2021. Some studies have revealed the existence of these vulnerabilities, but there is little quantitative research available, and there are no safeguard mechanisms to protect bridges from such attacks. Furthermore, no studies are available on the practices of cross-chain bridges that can cause financial losses. We propose \toolName~(Cross-Chain Watcher), a modular and extensible logic-driven anomaly detector for cross-chain bridges. It operates in three main phases: (1) decoding events and transactions from multiple blockchains, (2) building logic relations from the extracted data, and (3) evaluating these relations against a set of detection rules. Using \toolName, we analyze data from two previously attacked bridges: the Ronin and Nomad bridges. \toolName~was able to successfully identify the transactions that led to losses of \$611M and \$190M (USD) and surpassed the results obtained by a reputable security firm in the latter. We not only uncover successful attacks, but also reveal other anomalies, such as 37 cross-chain transactions (\CCTX) that these bridges should not have accepted, failed attempts to exploit Nomad, over \$7.8M worth of tokens locked on one chain but never released on Ethereum, and \$200K lost by users due to inadequate interaction with bridges. We provide the first open dataset of 81,000 \CCTXS~across three blockchains, capturing more than \$4.2B in token transfers.
• Different digitalization techniques in the energy sector is briefly outlined • Impacts of digitalization in smart grids is critically analyzed. • Integration of the renewable energy sources are given priority during the selection of digitalization methods. • Maximum energy efficiency attainment using demand response is ensured in the processes. Decarbonization, decentralization, and digitalization are essential for advanced energy systems (AES), which encompass smart grids, renewable energy integration, and demand response initiatives. Digitalization is a significant trend that transforms societal, economic, and environmental processes globally. This shift moves us from traditional power grids to decentralized, intelligent networks that enhance efficiency, reliability, and sustainability. By integrating data and connectivity, these technologies optimize energy production, distribution, and consumption. This article presents a comprehensive literature review of four closely related emerging technologies: Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, and Digital Twin (DT) in AES. Our findings from the previous works indicate that AI significantly improves Demand Response strategies by enhancing the prediction, optimization, and management of energy consumption. Techniques like linear regression effectively predict power demand and aggregated loads, while more complex methods such as Support Vector Regression (SVR) and reinforcement learning (RL) optimize appliance scheduling and load forecasting. The integration of IoT technologies into Energy Management Systems (EMS) further enhances efficiency and sustainability through real-time monitoring and automated control. Additionally, DT technology aids in simulating energy scenarios and optimizing consumption in both residential and commercial smart grids. Our findings also emphasize blockchain’s role in creating decentralized energy trading platforms, facilitating peer-to-peer transactions, and enhancing trust through smart contracts. The insights gained from this review highlight the essential role of these emerging technologies in supporting decentralized, intelligent energy networks, offering valuable strategies for stakeholders to navigate the complexities of the evolving digital energy landscape.
Software-Defined Networking (SDN) has emerged as a revolutionary architecture in computer networks, offering comprehensive network control and monitoring capabilities. However, securing the east–west interface, which is crucial for communication between distributed SDN controllers, remains a significant challenge. This study proposes a novel blockchain-based security framework that integrates Ethereum technology with customized blockchain algorithms for authentication, encryption, and access control. The framework introduces decentralized mechanisms to protect against diverse attacks, including false data injection, man-in-the-middle (MitM), and unauthorized access. Experimental results demonstrate the effectiveness of this framework in securing distributed controllers while maintaining high network performance and low latency, paving the way for more resilient and trustworthy SDN infrastructures.
G.B. Bhavana, R. S. Anand, J. Ramprabhakar, Veerpratap Meena · 6 authors
Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.
Juhani Latvakoski, Jouni Heikkinen, Jari Palosaari, Vesa Kyllönen · 5 authors
The aim of this research has been to enable the management of trustworthy relationships between stakeholders, service providers, and physical assets, which are required in critical energy and mobility cyber–physical systems (CPS) applications. The achieved novel contribution is the concept of trustworthy communities with respective experimental solutions, which are developed by relying on verifiable credentials, smart contracts, trust over IP, and an Ethereum-based distributed ledger. The provided trustworthy community solutions are validated by executing them in two practical use cases, which are called energy flexibility and hunting safety. The energy flexibility case validation considered the execution of the solutions with one simulated and two real buildings with the energy flexibility aggregation platform, which was able to trade the flexibilities in an energy flexibility marketplace. The provided solutions were executed with a hunting safety smartphone application for a hunter and the smartwatch of a person moving around in the forest. The evaluations indicate that conceptual solutions for trustworthy communities fulfill the purpose and contribute toward making energy flexibility trading and hunting safety possible and trustworthy enough for participants. A trustworthy community solution is required to make value sharing and usage of critical energy resources and their flexibilities feasible and secure enough for their owners as part of the energy flexibility community. Sharing the presence and location in mobile conditions requires a trustworthy community solution because of security and privacy reasons, but it can also save lives in real-life elk hunting cases. During the evaluations, the need for further studies related to performance, scalability, community applications, verifiable credentials with wallets, sharing of values and incentives, authorized trust networks, dynamic trust situations, time-sensitive behavior, autonomous operations with smart contracts through security assessment, and applicability have been detected.
Le Dung, Nguyen Sy Quan, Hoang Tuan Linh, Tran Thanh Son · 6 authors
In recent years, the demand for renewable energy sources has increased significantly, and microgrids have emerged as an effective solution for integrating renewable energy sources into the power grid. In microgrid systems, distributed control is crucial for ensuring reliable and efficient operation. However, conventional control systems have limitations such as a single point of failure, security vulnerabilities, and lack of trust between different stakeholders. A common type of attack is the false data injection attack (FDIA). The proposed system utilizes smart contracts and the Tangle, a data structure used by the Internet of Things Application (IOTA), to enable secure and efficient data exchange among different stakeholders in the microgrid. The system also employs the IOTA token, which allows for secure and transparent transactions among different parties. Ultimately, case studies are introduced to confirm that the proposed hierarchical blockchain system is a robust solution for securing the control system and enhancing the advantages for prosumers. The numerical results illustrate the effectiveness and practicality of the approach.
Software-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper introduces a novel approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system utilizes the IOTA Tangle, a directed acyclic graph (DAG) structure, to improve scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts: Authority, Access Control, and DoS Detector, to ensure trusted and secure network operations, prevent unauthorized access, maintain the integrity of control data, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for the further integration of blockchain technologies in network management.
Smart grid technologies have rapidly become one of the largest and most comprehensive sources of data for the modern utility. For the most part, data streams are seen as an essential tool that enable utilities to carry their day-to-day business operations, but they also create the need for efficient and secure data management strategies. In the context of the smart grid, ensuring data privacy is becoming an increasing concern due to a combination of factors that range from shifts in operational paradigms and rapid technology evolution to changes in legislation. Furthermore, researchers have highlighted the risks associated with improperly protected energy records. For example, energy consumption data from homes could be used to infer the behaviors and habits of home occupants through activity recognition or user profiling (Fan, 2017), which may lead to unfair service pricing, targeted advertising, or other personal security violations. Similarly, Electric Vehicles’ (EVs) charging metadata could be used to reveal private information about the owner such as their payment methods, preferred charging stations, and other locational and timing information that could be used to reconstruct the vehicle owner’s behaviors. The privacy of user data, even when used for statistical analysis or machine learning training processes, also needs to be carefully considered, as an individual’s private traits may still be vulnerable if their inclusion/exclusion greatly impacts the result or could be linked to a public dataset through cross-reference. The breach of user privacy also has severe impacts for organizations that store, transmit, or work on the data in the form of diminishing the public’s trust in them while potentially incurring legal consequences (e.g., fines and suspensions under the European Union General Data Protection Regulation, Health Insurance Portability and Accountability Act, etc.). Because of these risks, several privacy-preserving mechanisms are available to help organizations comply with privacy legislations and prevent the unauthorized and malicious use of user data. In light of these concerns, this report focuses on performing a computational review of privacy-preserving mechanisms that have received a significant amount of interest in literature. It specifically focuses on 1) homomorphic encryption, 2) zero-knowledge proofs, 3) differential privacy, and 4) federated learning. It is worth noting that although many of the methods presented in this document rely on cryptographic primitives, their intent is not to provide perfect secrecy, but rather to enable users to maintain privacy, and thus they shall not be compared or equated to other constructs that are aimed to address cybersecurity constructs.
Andrew R. Short, Τheofanis G Orfanoudakis, Helen C. Leligou
This research paper discusses security issues with current deployments of Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition Systems (SCADA) in the industry and proposes a solution that enables PLC devices to query a blockchain infrastructure for commands and setpoints. The blockchain assumes a dual role in this context: serving as an immutable audit trail database as well as a trusted source for critical commands and setpoints. In contrast to the conventional paradigm, this novel approach does not require write access at the PLC level, thus minimizes its attack surface and helping to protect against known and zero-day vulnerabilities often used in cyberwarfare, such as in the case of the notorious Stuxnet worm. Applications that enforce the logging of user operations for Good Manufacturing Practices (GMP) or compliance purposes use the blockchain network as an audit trail database for user actions. Any attempt to maliciously circumvent the logging operation would not affect the operation of a critical process. Additionally, a prototype implementation developed as part of this research finds that modern PLC devices are more than capable of interacting with private Ethereum blockchain nodes. The required libraries and user code consume a small percentage of available resources, while the duration of a complete request-response cycle measured around 22msec. The authors anticipate that PLCBlox can be used as a drop-in replacement for applications requiring higher security standards and logging enforcement, such as nuclear power plants or other critical infrastructure.
Gary Hahn, Emilio C. Piesciorovsky, Raymond Borges Hink, Aaron Werth
• Faulted phases detection using backup method that is external to protective relay. • Validates integrity of input data using distributed ledger technology. • Tested using simulated faults in a realistic electrical substation test bed. Modern electrical grids have intelligent electronic devices (IEDs) such as protective relays that use internal logic to detect the types of electrical faults. The increasing integration of distributed energy sources and the resulting complexity of electrical grid communication architectures necessitates enhanced robustness of IEDs’ monitoring while maintaining security against potential cyber threats. In this study, a backup electrical faulted phase detection method with a distributed ledger technology (DLT) platform was implemented. Cyber Grid Guard software was developed to collect phase currents and voltages transmitted through IEC 61850 GOOSE messages, detect faulted phases from the IEDs using the GOOSE data, and validate the data by hashing them and storing them in the distributed ledger. In this way, the hashed data were run into an electrical faulted phase algorithm based on using a current threshold for detecting the faulted phases in the medium-voltage main feeder of an electrical substation. The detection of the electrical faulted phases was assessed in a real-time simulator with protective relays, meters, the software framework, and DLT in the loop. The proposed method provides secure and reliable backup detection external to the IEDs, and DLT validation enhances system security and trust.
Pavan Ramchandra Padghan, Arul Daniel Samuel, Raja Pitchaimuthu
Cooperative power-sharing (CPS) is one of the cutting-edge power management strategies the power system planners contemplate transforming from its traditional structure to a more decentralized framework as the globe prepares for a future with low carbon emissions. Cooperative microgrids are considered one of the future power system configurations where individual microgrids (MGs) can support each other through CPS. This paper introduces a decentralized energy market (DEM) to operate CPS hierarchically between MGs, groups of MGs, and the utility grid. The security of power-sharing transactions and the privacy of MGs pose challenges in terms of confidentiality and data integrity. To deal with these issues, a DEM framework based on distributed ledger technology (DLT) is proposed to ensure power transactions between cooperative MGs with a guarantee. A scheme is built on the Ethereum testnet to estimate the performance and validation of the proposed smart contracts. A case study with actual data for a group of four MGs is presented to validate the effectiveness of the proposed framework. The proposed blockchain approach results in money transactions resulting in MG1 receiving $485; while MG2 gets $525, MG3 and MG4 generates a revenue of $435 and $362.5 respectively. The numerical results demonstrate that MGs can actively share power with peers and achieve economic benefits.
Khadija Begum, Md Ariful Islam Mozumder, Moon-Il Joo, Hee‐Cheol Kim
The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback-Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.
Information and Communication Technology (ICT) provides customers with utilities and smart grid solutions, enabling enhanced monitoring and control of energy management systems. This technology is poised to elevate the reliability, sustainability, and efficiency of future electric grids through the implementation of advanced metering infrastructure (AMI). However, current Supervisory Control and Data Acquisition (SCADA) systems lack trusted machine authentication in smart grid communications, leaving the electric grid vulnerable to cyberattacks via sophisticated network technologies such as wireless access points, sensors, routers, and gateways. Therefore, ensuring proper management of data integrity from field sensors is crucial to enhance the reliability of SCADA systems. In this context, the utilization of quantum key distribution (QKD) key pairs is proposed to uphold integrity in smart grid communications. This paper presents a fibre optic blockchain network designed to manage and utilize cryptographic keys, facilitating the authentication of peer-to-peer (P2P) communications in SCADA systems. This demonstration underscores the feasibility of employing QKD and blockchain to further strengthen the integrity and authentication of smart grid communications. Additionally, this paper delves into discussing the performance metrics and overhead expenses of the proposed scheme in comparison with existing state-of-the-art proposals. Simulation results highlight the significant impact of blockchain size on the system setup’s throughput and latency.
A mempool is a security-critical subsystem in a public blockchain. Recent mempool attacks, notably asymmetric DoS, have shown their ability to severely damage the Ethereum network. This paper tackles the open research problem of designing principled and non-intrusive defenses against asymmetric mempool DoSes with provable security. It presents the first mempool economic-security definitions based on mempool-observable conditions. It then presents SAFERAD, a framework of secure mempool designs with provable security against asymmetric DoSes. To defend against dual attacks by evicting and locking a victim mempool, SAFERAD adopts a non-trivial design of enforcing an upper bound of the attack damage under the locking attacks and a lower bound of the attack cost under the eviction attacks. With a prototype implementation on Geth and evaluation under real transaction traces, the results show SAFERAD has low overhead in latency and block revenue, implying non-intrusiveness and practicality.
Muhammad Faheem, Basit Raza, Muhammad Shoaib Bhutta, Syed Hamid Hussain Madni
Abstract The rapid and green energy transition is essential to deal with the fast‐growing energy needs in both public and industrial sectors. This has paved the way to integrate distributed renewable energy resources () such as solar, hydro, wind, and geothermal into the power grid (). Wind and solar are free, zero‐carbon emission, and everlasting power sources that contribute 5% and 7% of global electricity generation, respectively. Therefore, the fast, secure, and reliable integration of these green is critical to achieve the instant energy demands. Smart grid due to inherited characteristics such as intelligent sensing, computing, and communication technologies can effectively integrate the . However, the existing smart grid communication architecture faces various cyberattacks, resulting in poor integration, monitoring, and control of . In this respect, blockchain technology can provide fast, secure, and efficient end‐to‐end communication between in the smart grid. In this study, the authors propose a blockchain‐based resilient and secure scheme called for wireless sensor networks ‐based events monitoring and control in . Experimental studies and performance analyses are carried out to predict the efficiency of the proposed scheme by considering numerous standard metrics. The extensive numerical results demonstrated that the proposed scheme is significant in terms of secure, resilient, and reliable information transmission for in .
Yousef Methkal Abd Algani, Vuda Sreenivasa Rao, R Saravanakumar
Decentralized energy transactions in smart grids are gaining momentum as a solution to the challenges posed by traditional centralized energy systems. This paper presents an AI-powered approach to enhancing the smart grid's energy transactions' efficiency and security. Building upon the principles of decentralization and artificial intelligence (AI), the method leverages block chain technology and advanced AI algorithms to optimize energy distribution while ensuring robust security measures. The existing centralized energy systems face significant vulnerabilities, including single points of failure and susceptibility to cyberattacks. In contrast, decentralized energy transactions facilitated by block chain technology distribute transaction records across multiple nodes, mitigating the risk of manipulation and enhancing system resilience. The proposed method integrates AI algorithms to analyse diverse data sources, such as energy consumption patterns and grid performance metrics, enabling real-time optimization of energy distribution. By dynamically adjusting energy allocation based on predictive analytics, the system minimizes wastage and reduces transmission losses, thereby improving overall efficiency. The security of energy transactions is fortified through cryptographic measures provided by block chain technology. Smart contracts automate and enforce transaction rules, eliminating the requirement for middlemen and lowering the possibility of fraud. Furthermore, the decentralized nature of the approach fosters transparency and accountability, as all transactions are recorded on a transparent ledger visible to all participants. This transparency enhances trust among stakeholders and facilitates incorporating renewable energy sources and distributed energy resources into the grid. Overall, the AI-powered secure decentralized energy transaction framework offers a promising pathway towards a more resilient, efficient, and sustainable energy infrastructure for smart grids.