Blockchain Papers

Mohamed Boudra, Ahmed Bendahmane

No abstract is available for this record.

Abdulhamid Musa

Purpose: Nigeria sits on massive renewable potential, yet clean power barely trickles into the national grid. This paper digs into why the transition keeps stalling despite the Electricity Act 2023 handing states the keys to their own power markets. Rather than celebrating the new legal framework, it examines the commercial and technical friction that is blocking developers from connecting to the grid. Methodology: This study used a qualitative policy review to examine Nigeria's renewable energy regulatory framework by reviewing key legal documents alongside relevant academic and industry publications. The selected materials, published mainly between 2023 and 2026, were examined through a structured narrative analysis to identify policy gaps affecting renewable energy policy readiness. Findings: The findings show that Nigeria has made important legal and policy progress, but implementation remains weak. The electricity market is fragmented, and renewable-energy developers continue to face challenges such as unclear federal-state coordination, limited grid capacity and flexibility, non-cost-reflective tariffs, and insufficient use of smart-grid, storage, and circular-economy technologies. The preliminary assessment produced a readiness score of 2.33 out of 6, suggesting that while policy ambition is evident, the conditions needed for effective market delivery are still inadequate. Unique Contribution to Theory, Practice and Policy: The six-pillar framework gives researchers a concrete diagnostic for measuring transition readiness beyond checkbox compliance. For industry players, it highlights exactly where projects get stuck between permitting chaos and unbankable contracts. For policymakers, the paper makes the case for binding federal-state coordination treaties, mandatory storage and digital standards, aggressive mini-grid scaling, and placing consumer affordability at the absolute center of market design rather than treating it as an afterthought.

MARBIYAT TAHIR GIDADO, BASHIRU ABDULGANIYU, MOHAMMED NASIR MUSA, Umaru Umaru

The increasing digitalization of smart grids has significantly improved the efficiency, reliability, and sustainability of modern power systems. However, the integration of advanced technologies, such as artificial intelligence, the Internet of Things, and cloud computing, has introduced new cybersecurity vulnerabilities that threaten critical energy infrastructure. This study presents a blockchain-enabled privacy-preserving Artificial intelligence framework designed to enhance cybersecurity in smart grid environments, with a particular focus on Northeast Nigeria as a case study. The framework integrates blockchain technology, federated learning, differential privacy, edge computing, and artificial intelligence (AI)-driven intrusion detection into a unified architecture to provide secure, intelligent, and privacy-aware protection for smart grid systems. The proposed framework was developed using the design science research methodology and evaluated through simulation and comparative performance analysis. The framework achieved excellent detection performance with an accuracy of 96.8%, precision of 95.9%, recall of 96.4%, and F1-score of 96.1%, significantly outperforming conventional centralized AI and blockchain-only approaches. The integration of federated learning and differential privacy effectively protected consumer information with a privacy leakage rate of only 2.7% while maintaining high model utility of 94.8%. The blockchain performance evaluation showed a transaction latency of 184.6 Ms, a throughput of 421.3 transactions per second, and efficient smart contract execution. The suitability of the framework for practical deployment with moderate resource requirements by computational assessment. The findings demonstrate that combining blockchain, privacy-preserving learning, and AI provides a comprehensive, scalable, and resilient cybersecurity solution for SGIs. This study contributes to the growing body of knowledge on smart grid cybersecurity and offers practical insights for utility providers, researchers, and policymakers seeking to strengthen the security and resilience of emerging smart grid systems, particularly in developing regions with infrastructural challenges.

Nattawat Songsom

No abstract is available for this record.

Simon Poltak Hamonangan Hutabarat

No abstract is available for this record.

Nadir Subaşı, Özen Özer

This chapter explores the critical role of data polishing and anomaly detection in enabling decentralized finance (DeFi)-driven digital transformation within the energy and utilities industry, with broader implications for dataintensive environments such as cryptocurrency markets and metaverse ecosystems. In an ideal digital infrastructure, decision-making systems operate on transparent, consistent, and high-quality data that support reliable automation, decentralized governance, and predictive analytics. Such an ecosystem presumes seamless data integrity, adaptive risk monitoring, and trustworthy financial and operational exchanges. In practice, however, industrial and financial platforms remain vulnerable to noisy datasets, measurement errors, systemic inconsistencies, and undetected anomalies, which undermine analytical accuracy and institutional confidence. Prior studies on machine learning, data cleaning, and blockchain-based energy systems emphasize preprocessing, normalization, and outlier detection as 78 prerequisites for intelligent operations. Parallel research on crypto-market anomalies and metaverse security highlights the relevance of statistical and learning-based surveillance models. Yet, these strands often remain methodologically fragmented, rarely examining their integrated function within DeFi-enabled infrastructures. This chapter addresses this gap by advancing a unified analytical framework grounded in data reliability theory and decentralized analytics. Focusing on the comparative evaluation of IQR, MAD, and LOF models applied to XRP/USD datasets since 2018, the paper assesses robustness, sensitivity, and computational efficiency. The findings demonstrate how systematic data polishing strengthens trustless financial architectures, enhances operational resilience, and supports sustainable digital transformation in energy and utility ecosystems.

Craig Wright

A tokenised energy market settles payment against metered dispatch, but the meter reading is the prosumer's private information: a self-interested prosumer can report more energy than it supplied and be paid for the difference. The companion papers in this programme assume meter integrity — truthful reporting — and build settlement, participation, and delivery contracts on top of it. This paper derives the verification contract that makes the assumption hold. A prosumer dispatches a quantity it observes privately and reports a possibly inflated figure to the settlement layer; the grid-telemetry layer can audit a report at a cost, detecting a discrepancy with a probability that reflects sensor accuracy, and a detected misreport forfeits a posted verification stake. We treat the audit probability, the stake, and the sensor accuracy as the designer's instruments and characterise the verification that makes truthful reporting weakly dominant at minimum cost. The baseline assumes a margin-independent detection probability and one-sided audit error (false negatives possible, false positives excluded); both are stated and the general margin-dependent condition is given. First, truthful reporting is weakly dominant if and only if the expected forfeiture covers the largest gain from admissible over-reporting, αφB ≥ Pm̄ (strict under strict inequality), where α is the audit probability, φ the per-audit detection probability, B the stake, and m̄ the largest admissible over-report; with a one-unit maximum this is αφB ≥ P (Proposition 1). Second, along this deterrence frontier the audit probability is α = Pm̄/(φB), and once the stake is itself chosen against its capital carry the least-cost interior contract is B* = √(κPm̄/(ρφ)), α* = √(ρPm̄/(κφ)), total cost 2√(ρκPm̄/φ), all decreasing in detection accuracy, so accurate telemetry drives the audit rate, the stake, and the cost down together (Theorem 1). Third, sensor accuracy is itself a procurable instrument with a convex capital cost, and the cost-minimising accuracy equates marginal sensor capital cost to the marginal audit-opex saving, a capex–opex frontier between better meters and more auditing (Proposition 2). Fourth, the per-report enforcement αφB is exactly the meter-integrity guarantee the companion papers assume; truthful reporting is weakly dominant on the binding frontier and strict under an arbitrarily small slack, so the reported quantity equals the dispatched quantity, discharging that assumption from primitives and closing the stack at its base (Proposition 3). Full proofs are in the online appendix.

Dr.M.Sukesh Dr.M.Sukesh, MANCHIKANTI YASHASWINI, GADE SHARATH, GUGULOTHU NAVEEN · 5 authors

The increasing number of behind-the-meter distributed energy resources (DERs) is changing traditional distribution systems in a big way by adding new ways to control and monitor them. But the effectiveness and dependability of these systems depend heavily on the accuracy of the data (like measurements, control commands, etc.) that the prosumers, aggregators, and grid operators share with each other. In addition, traditional power systems rely entirely on trusted aggregators to gather data from these DERs. If these aggregators are hacked, the whole system could be at risk. In this paper, we respond to these concerns by suggesting a hierarchical blockchain-based framework that includes a distributed integrity auditing system for measuring DERs. By using hash functions and Merkle trees, a secure and lightweight blockchain-based hash aggregation protocol is made to make sure that behind-the-meter DERs' measurements are real. Also, an automated distributed sanity check of DERs' set points (control commands) is suggested to lower the risk of coordinated cyber attacks on a large number of DERs. The suggested framework is put into action and tested in a number of different situations to see how well it works and how safe it is. The results show that the framework can handle more work because it can cut its runtime and storage costs by about 47% and 44%, respectively.

Yizhou Chen, Zeyu Sun, Guoqing Wang, Dan Hao

Deep neural networks (DNNs) are one of the most effective methods available for detecting smart contract vulner-abilities (SCVs). The performance of current DNN approaches relies heavily on a large number of training samples and labels. The semi-supervised learning (SSL) trains the pseudo-labeling mechanism and performs label propagation on unlabeled data, thereby mitigating this problem. However, these approaches are not suitable for tasks related to Smart Contract Vulnerability Detection (SCVD) due to their limitations in capturing subtle faults, which are the primary causes of SCVD. We believe that subtle faults are contained in the correlation feature, which encompasses both commonalities among vulnerable contracts and differences between vulnerable and non-vulnerable contracts. Therefore, we propose a correlation-driven SSL method called Jupiter to solve this limitation. Specifically, Jupiter incorporates a contrastive learning module that conducts pairwise comparisons between smart contracts and captures correlation features. These correlation features facilitate the separation of feature distributions in vulnerable and non-vulnerable contracts. Then, a support vector machine with a built-in RBF kernel function is utilized to establish a decision boundary between the two types of contract distributions. We employ the distance from data to the decision boundary as a confidence score and propagate high-quality pseudo-labels to each unlabeled data surpassing the threshold. This process is iterated, where the labeled dataset is constructed using a combination of pseudo-labeled and reallabeled data to be re-input into the contrastive learning model until all data is labeled. Finally, the combination of semantic information and correlation features accurately detects SCVs.By conducting an empirical evaluation on a large-scale realworld dataset comprising over 40,000 smart contracts, we compare the performance of 6 state-of-the-art SSL methods and 5 state-of-the-art SCVD methods. Our findings demonstrate the effectiveness of our proposed method, Jupiter, in two key aspects: (1) Jupiter achieves optimal performance across all SSL methods, outperforming them by 18.89% to 28.42% in terms of F1- score; (2) Current state-of-the-art SCVD methods fail to deliver satisfactory results when only small amounts of labeled data are available. Specifically, these methods achieve F1-scores ranging from 49.72% to 50.98% when utilizing just 10% of the labeled data. In contrast, under the same conditions, Jupiter outperforms all baselines, achieving an F1-score of 89.28%, which represents an improvement of 75.12% to 79.56%.

C.Madhusudhana Rao, Praveen Kumar Naidu Rayanki, Polepalli Rajeev Meenon, Salapakshi Jai Kumar · 5 authors

Massive growth of the Chinese carbon-credit market has been characterized by endemic data obscurity, certification latency, and fraud, specifically in the Passenger Cars Corporate Average Fuel Consumption and New Energy Vehicle Credit Regulation (PCFN) scheme in the automotive industry. Its present centralized management system is not transparent and has information asymmetries and is very prone to manipulation results in erroneous carbon-credit determination, inefficient dealings, and the deteriorating stakeholder confidence. This paper offers a federated blockchain-IoT information infrastructure to deal with these severe inadequacies, namely, the provision of end-to-end transparency, tamper-resistance, and autonomous functionality in managing carbon-credit. The framework uses radio frequency identification (RFID) to capture real-time emission data, delegated proof-of-stake (DPoS) consensus to provide scalable verification and uses smart contracts to provide a decentralized credit assessment and trading. Besides, an AI-based predictive analytics control is incorporated to dynamically predict credit prices and identify anomalies on distributed nodes. Experimental comparison with national automotive carbon datasets reveals that the 72.6% latency of credit verification is reduced, the 38.2% transparency of audit is increased, and the 93.5% accuracy of fraud detection is achieved rather significantly in comparison to the traditional centralized model. The suggested framework will offer a platform on which the cross-sector carbon-credit markets of China can be scaled and verified to speed up the process of the country achieving its carbon neutrality targets of 3060.

Haoxin Sun, Yu Xiao, Jiale Li, Yiwen Xu · 8 authors

Since the advent of smart contracts, security vulnerabilities have remained a persistent challenge, compromsing both the reliability of contract execution and the overall stability of the virtual currency market. Consequently... | Find, read and cite all the research you need on Tech Science Press

Yves Pircher

The hydropower fleet in Austria is ageing and needs to be modernised to adapt to changing conditions in national and international energy systems. The financial viability of hydropower repowering projects remains a challenge because of high investment costs and long payback periods. A part from additional revenuestreams, a Bitcoin mining operation has the potential to be used as a flexible demand source also for curtailment and grid stability services. This thesis provides quantitative evidence on whether a Bitcoin mining operation can serve as an additional revenue stream to improve the investment metrics of a hydro repowering project in Austria, using a dynamic investment calculation and sensitivity analysis.The results show that Bitcoin mining can improve the financial performance especially for run-of-river plants with higher full load hours. These positive effects are sensitive to the volatility of the Bitcoin price and the network hash rate, making long-term returns difficult to predict.

Ade Indriawan, Nur Aini Rakhmawati

The rise of non-fungible tokens (NFTs) has increased the risk of fraud and market manipulation. This study introduces a method for detecting wash trading in the NFT marketplace using Graph Neural Networks (GNNs) applied to Ethereum blockchain transaction data. We constructed a heterogeneous graph, used Depth-First Search for labelling, and extracted graph features, including PageRank and degree centrality. We evaluate various classification models: Multilayer Perceptron (MLP), Graph Convolutional Neural Network (GCN), and Heterogeneous Graph Convolutional Neural Network (HeteroGCN). The results show that GNN models, particularly the feature-enhanced HeteroGCN, exhibit superior performance compared to featureless models and traditional tabular baselines. The key contribution of this study is that PageRank and Degree Centrality features significantly improve the accuracy of identifying transactions involved in market manipulation.

Hengxin Lei, Thein Lai Wong, Tong Ming Lim, XiangFu Zhao · 6 authors

Smart contracts manage billions of dollars' worth of digital assets. Once vulnerabilities are exploited, they may lead to fund theft, transaction rollback, or asset freezing. The current machine learning based smart contract vulnerability detection methods have poor performance and high time complexity in detecting sparse labeled data. We propose a smart contract vulnerability detection model for supervised Bigram-Principle Component Analysis (Bi-PCA). Supervising Bi-PCA can utilize labeled vulnerability data for supervised dimensionality reduction. The supervised Bi-PCA model can utilize the information from these additional labels to accurately extract more interpretable potential structures. This detection model is universal and can be used in industrial scenarios on all labeled datasets. The experimental results show that it has high accuracy and recall while maintaining the feature of the original label data.

Phitiwat Lopyim

Bitcoin mining business has recently gained attention, especially after reports of electricity theft within MEA area for Bitcoin mining purposes. This issue has become a major concern for the MEA distribution system. It’s estimated that non-technical losses (electricity thief) in the MEA distribution system have increased by 0.20-0.30 percent, with annual losses 8 to 15 million USD in revenue. This impact underscores the urgent need for a deeper examination of Bitcoin mining as a new form of investment, raising questions about crypto currencies and profitability for Bitcoin mining business. Illegal bitcoin mining units have diverted significant electricity through illicit connections and mining equipment by adapting meter and cable line through MEA work area and do not pay power charges. Adapted electrical system that are not up to standard can lead to short circuit, Fires, and electric shocks. This instability can harm both the lives and properly of electricity users. This abstract are delves on landscape of risks by Illegal Bitcoin mining units activities, highlighting the challenges they present to regulatory compliance and organizational resilience. Strategies to mitigate these risks and enhance operation control are also discussed.MEA has conducted a risk assessment of the above case according to the COSO ERM Framework. This assessment was carried out through a high-level management meeting and has escalated the risk to corporate level that requires urgent management action to prevent recurrence. The approach have five key actions : 1) Identify electricity theft methods by using "ArcGIS Collector" application to pin suspected location. 2) Assess the damages. 3) Coordinate with relevant agencies for legal action. 4) Develop preventive and corrective measure by install Online Load Monitoring (OLM) 5) Report to the executive. This approach ensures a comprehensive handling of electricity theft incidents, integrating detection, assessment, legal action, prevention, and improvement measures within risk management framework

Bhabendu Kumar Mohanta, Ali Ismail Awad, Tarek Elsaka, Hamza Kheddar · 5 authors

Intelligent devices with embedded technology have proliferated dramatically over the past decade. The Internet of Things (IoT) has emerged as a transformational force, advancing traditional systems to previously unattainable levels of intelligence. Smart cities, transportation, healthcare, supply-chain management, agriculture, water management, and smart grid (SG) systems are among the industries where the IoT has found applications. These developments are demonstrated by the integration of IoT systems into SG networks, offering significant improvements in sustainability, dependability, and efficiency. Such systems use various IoT devices to continuously monitor the environment and transmit data for processing and analysis. Nonetheless, the growth of the IoT has introduced security vulnerabilities, including concerns about user identification, data integrity, and trust, especially in SG applications. This study aims to resolve several security challenges in IoT-enabled SG applications to support sustainability. The proposed scheme effectively tackles critical security requirements such as data integrity, user anonymity, distributed storage, trust management, and decentralized architecture. The security concerns addressed by blockchain technology include preserving data integrity, fostering trust, providing secure communication, and enabling effective monitoring. Smart contracts automate system processes and are effective in maintaining user trust. The experimental findings support the viability of the proposed system, demonstrating a computational cost of 3.150 ms and a communication overhead of 992 bits, both representing improvements over various existing solutions. Additionally, the deployment cost for the smart contract is found to be 5.64 USD with a writing cost of 2.89 USD, both of which are lower than the costs associated with comparable approaches.

Fatih Ertam

Blockchain technologies have profoundly transformed information systems by providing decentralized infrastructures that enhance transparency, security, and traceability. Ethereum, in particular, supports smart contracts and facilitates the development of decentralized finance (DeFi), non-fungible tokens (NFTs), and Web3 applications. However, its openness also enables illicit activities, including fraud and money laundering, through anonymous wallets. Identifying wallets involved in large transfers or abnormal transactional patterns is therefore critical to ecosystem security. This study proposes an AI-based framework employing XGBoost, LightGBM, and CatBoost to detect suspicious Ethereum wallets, achieving test accuracies between 95.83% and 96.46%. The system provides near real-time predictions for individual or recent wallet addresses using a pre-trained XGBoost model. To improve interpretability, SHAP (SHapley Additive exPlanations) visualizations are integrated, highlighting the contribution of each feature. The results demonstrate the effectiveness of AI-driven methods in monitoring and securing Ethereum transactions against fraudulent activities.

Anushree A. Aserkar, K. Chanthirasekaran, P. Anitha, Gaurav Goyal · 6 authors

No abstract is available for this record.

B. T. King, Srijib Mukherjee

Over the past few years, a strange industrial electricity customer has taken the industry by storm: Bitcoin mining. In 2021, the nascent Bitcoin mining industry, which had primarily been in China, shipped massive amounts of hardware and opportunity to capture global market share to the United States.

Saviz Changizi, Nasibeh Mohammadzadeh, Mohammad Shojafar

This paper presents a first-of-its-kind modular AI framework for telecom fraud detection, integrating machine learning (ML), large language models (LLMs), and blockchain smart contracts to unify statistical classification, semantic reasoning, and decentralized enforcement. A synthetic dataset of 100 users across 300 sessions in Birmingham, UK, simulated telecom usage with$\mathbf{1 \% - 5 \%}$injected fraud, including GPS spoofing, excessive transmission power, and prolonged usage. Seven ML models were trained, with Random Forest optimized using a precision-recall threshold of$\mathbf{0. 7 2 1 7}$. Six configurations varied the decision logic between ML and GPT-4o-based LLMs, with LLMs performing context-aware reasoning via behavioral prompts. Solidity smart contracts on a local Ethereum network enforced decisions, mapping users to blockchain identities with a Proof-of-Stake-style validation mechanism. The ML-only configuration achieved 92.25 % accuracy with perfect user-level precision and recall, while LLM variants enhanced behavioral and temporal reasoning. This framework advances robust and explainable fraud detection for future telecom infrastructures.

Yazeed Yasin Ghadi, Tehseen Mazhar, Tariq Shahzad, Ines Hilali Jaghdam · 7 authors

This study delves into the vulnerability of the smart grid to infiltration by hackers and proposes methods to safeguard it by leveraging blockchain and artificial intelligence (AI). A categorization and analysis of cyberattacks against smart grids will be conducted, focusing on those targeting their communication layers. The main goal of the work is to address the challenges in this area by implementing novel detection and defense strategies. The authors categorize attacks on smart grid networks based on the communication classes they want to compromise. They propose novel taxonomies specifically designed to detect and implement defense strategies. The study investigates artificial intelligence and blockchain techniques to identify cyber-attacks that employ deceptive data injection. The study indicates that cyberattacks against smart grids are increasing in frequency and complexity. The paper proposes innovative strategies for defense, such as enhancing cybersecurity with artificial intelligence and blockchain technology. The research further enumerates several challenges, such as counterfeit topological data, imprecise data identification, and combining big data with blockchain technology. Given the increasing risks, the study emphasizes the crucial need for robust cybersecurity safeguards in smart grids. This work contributes to the protection of smart grid infrastructures by categorizing attacks, suggesting novel defenses, and exploring solutions integrating artificial intelligence and blockchain technology. Research should prioritize enhancing technology to maximize security and counter emerging attack methods. The intended audience of our paper comprises graduate-level academics and independent researchers.

Shijie Ji, Mingyang Lei, Zhuyu Shi, Weiming Hu · 5 authors

This paper presents the Federated Learning-Enhanced Distributed Ledger Framework (FL-DLF), a novel approach to address data rights confirmation in the evolving electricity market characterized by distributed energy resources and virtual power plants. The FL-DLF integrates blockchain technology with federated learning to ensure data integrity, security, and privacy, enabling decentralized data processing and model training without compromising sensitive information. By implementing advanced cryptographic methods, access controls, and continuous monitoring, the framework provides a secure and efficient solution for data rights management. Its modular architecture allows for seamless integration with current systems and adaptability to future technologies. The paper’s contributions include proposing a secure ecosystem for data asset ownership verification, introducing a comprehensive technical solution integrating blockchain and encryption, and exploring the use of smart contracts for automated and efficient data transaction processes. The FL-DLF addresses the challenges of data integrity, privacy, and infrastructure dependability, offering a robust solution for the modern electricity market’s complex data rights confirmation needs.

K. Praveen Kumar, Shaik Lubna, Pullagurla Tharun Kumar

The decentralized nature of Ethereum exposes it to phishing, Ponzi schemes, and money laundering. Traditional fraud detection methods fail to identify complex patterns in transactions. This paper proposes a deep learning model based on Bi-Directional Long Short-Term Memory (Bi-LSTM) and Attention Mechanism for enhancing fraud detection accuracy in Ethereum transactions. The model handles sequential transaction data and employs Bi-LSTM to learn temporal correlations and Attention to select appropriate features. On a Kaggle Ethereum dataset, the model achieved 97% accuracy, 97% precision, 97% recall, and a 97% F1-score, much higher than existing works. The study demonstrates the usefulness of deep learning for the security of blockchain systems, having a robust process for realtime fraud detection.

Yitao Zhao, Xinglong Liu, Yiming Zhang, Jiahao Li

Aiming at the problems of data security and privacy protection in the traditional power metering data sharing mode, this paper puts forward an architecture design of power metering data sharing platform based on blockchain, and deeply analyzes its multi-party security. The platform adopts hierarchical architecture, including data layer, network layer, consensus layer, contract layer and application layer. The data can not be tampered with and can be traced through distributed ledger technology, and the improved DPoS consensus algorithm and intelligent contract technology are used to ensure data consistency and automatic processing. In terms of security, differential privacy, zero-knowledge proof and improved PBFT fault-tolerant model are adopted to effectively resist data tampering, unauthorized access and potential attacks. Through case analysis, the results show that the platform has obvious advantages in data integrity protection, access control, privacy protection and inter-agency collaboration efficiency improvement, and shows good adaptability in resource consumption. The research shows that blockchain technology provides a safe, efficient and reliable solution for power metering data sharing.