Traditional finance and crypto aren't just different systems-they're different paradigms speaking different languages. Traditional banks measure processes in days, compliance in paperwork, and access in restrictions. Cryptocurrency platforms promise instant transactions but often at the cost of compliance frameworks that traditional institutions require. These systemic limitations in both traditional and cryptocurrency systems highlight the critical need for a unified approach. Using STORE's decentralized cloud computing protocol as an example, this paper emphasizes a different future as it demonstrates the feasibility and impact of automated compliance through the transformative CLEAR framework. Our dual-database architecture achieves authenticated regulatory compliance at global scale, with our implementation achieving 55.6 seconds (verified) for complete end-to-end transactions, with KYC verification (18.84s), document processing (14.24s), and payment settlement (6.55s). This transforms what traditional finance considers a weeks-long journey into a seconds-long verification, without compromising the compliance standards that make global finance possible. It's not just faster-it's fundamentally re-imagined.
This study presents a novel system for simulating an energy community utilizing advanced tools such as Simulink and the Ethereum Sepolia blockchain. Simulink is employed to simulate energy transactions within the community, while the Ethereum Sepolia blockchain is used to securely record these transactions. The primary objective of this study is to assess the feasibility of establishing an energy community that utilizes a public blockchain, such as Ethereum Sepolia, within a simulated environment using the Simulink tool. This research is distinctive because no previous study has utilized a public blockchain for this type of community model through a simulation tool like Simulink. Public blockchains, while offering numerous advantages, often suffer from high transaction costs. This study aims to address this challenge by proposing a solution that can reduce these costs without compromising security or decentralization.
This comprehensive article explores the transformative impact of blockchain technology on energy trading risk management systems. The article examines how blockchain addresses critical challenges in data security, transparency, and regulatory compliance within the energy sector. Through detailed analysis of distributed ledger infrastructure, smart contract integration, and cryptographic security measures, the research demonstrates significant improvements in operational efficiency, transaction processing, and risk mitigation. The investigation encompasses automated compliance frameworks, data privacy mechanisms, and scalability solutions, highlighting how blockchain technology enhances market participation while reducing operational costs. The article also evaluates emerging technologies and industry standards, providing insights into future developments that will shape secure and efficient energy trading operations.
Distributed ledger technology (DLT), commonly referred to as blockchain, is emerging as a transformative technology solution for managing Virtual Power Plants (VPPs), which aggregate distributed energy resources (DERs). Smart contracts are a key feature of DLT that may be leveraged to streamline complex tasks, from resource onboarding to real-time market transactions, ensuring data reliability, interoperability, and transparency. Through adoption of DLT, utilities may enhance operational efficiency and stakeholder trust, creating secure, transparent ledgers of energy information exchanges, controls and transactions accessible to energy providers, consumers, regulators and other system stakeholders.The article provides an overview of the technology features, and an accessible and practical roadmap for integrating DLT into energy systems, outlining key steps from initial planning and transaction selection to deployment. Emphasizing real-world applicability, it illustrates DLT’s pivotal role in modernizing energy grids to meet evolving demands and sustainability goals.
Dong Liu, Juan S. Giraldo, Peter Pálenský, Pedro P. Vergara
Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and online sharing, we introduce a privacy preserving PF calculation framework, composed of two local strategies: a local randomisation strategy (LRS) and a local zero-knowledge proof (ZKP)-based data collection strategy. First, the LRS is used to achieve irreversible transformation and robust privacy protection for active and reactive power data, thereby ensuring that personal data remains confidential. Subsequently, the ZKP-based data collecting strategy is adopted to securely gather the training dataset for the ANN, enabling SMs to interact with the distribution system operator without revealing the actual voltage magnitude. Moreover, to mitigate the accuracy loss induced by the seasonal variations in load profiles, an incremental learning strategy is incorporated into the online application. The results across three datasets with varying measurement errors demonstrate that the proposed framework efficiently collects one month of SM data within one hour. Furthermore, it robustly maintains mean errors of 0.005 p.u. and 0.014 p.u. under multiple measurement errors and seasonal variations in load profiles, respectively.
Paula Heess, Stefanie Holly, Marc-Fabian Körner, Astrid Nieße · 9 authors
Abstract The need to harness the flexibility of small-scale assets for system stabilization, including redispatch, is growing rapidly with the increasing prevalence of distributed generation, such as photovoltaic systems and heavy loads, in particular heat pumps and electric vehicles. Integrating these resources into the redispatch process presents special requirements: On the one hand, building trust with the owners of such assets requires privacy and a reasonable degree of autonomy and engagement. On the other hand, besides the system’s scalability and robustness, the verifiability and traceability of provided data are essential for grid operators who depend on the reliable provision of redispatch services. To date, research and practice have encountered significant challenges in defining a system that enables the inclusion of decentralized flexibilities while satisfying necessary requirements. To that end, we present a novel conceptual system design that addresses these challenges by combining a multi-agent system (MAS) approach with verifiable information flows through digital self-sovereign identities (SSIs) and Zero-Knowledge-Proofs (ZKPs). Single agents, as edge devices, operate locally and autonomously, respecting customer preferences, while MAS provide the ability to design robust, reliable, and scalable systems. SSI enables agents to manage their data autonomously, while ZKPs are used to protect users’ privacy through selective data disclosure which allows the verification of the correctness of information without disclosing the underlying data. To validate the feasibility of this design, a case study is included to demonstrate the functionality of key sub-processes, such as baseline optimization, aggregation, and disaggregation, in a realistic scenario. This case study, supported by a prototype implementation, provides initial evidence of the concept’s soundness and lays the groundwork for future evaluation through extensive simulations and field testing. Together, the technologies included in the conceptual system design balance full transparency for grid operators with autonomy and data economy for asset owners.
The rise of cryptocurrencies and decentralized fi- nance (DeFi) has highlighted the importance of secure and collaborative management systems for digital assets. Multi-party crypto vaults provide a way to ensure distributed control, privacy, and fault tolerance by involving multiple participants in the management and approval of transactions. This paper explores the two primary approaches—Multi-Signature (Multi-Sig) and Multi-Party Computation (MPC)—that underpin these vaults. Multi-Sig schemes offer fast, scalable solutions for real-time applications, while MPC ensures strong privacy and security by allowing encrypted computations without exposing private keys. In addition key management models, such as split-key and blockchain-based methods, and fault tolerance mechanisms, including social recovery and time-locked protocols, which ensure that vault systems remain secure and operational even in cases of participant failure. This paper recommends MPC as the optimal approach for high-security, privacy-sensitive applications, such as institutional custody and financial systems, while outlining av- enues for future research, including post-quantum cryptography and latency reduction in MPC protocols. Index Terms—Multi-Party Computation (MPC), Multi- Signature (Multi-Sig), Crypto Vaults, Threshold Cryptography, Blockchain Key Management, Decentralized Finance (DeFi),, Threshold ECDSA, Social Recovery Mechanism, Time-Locked Withdrawal Protocol, Fault Tolerance in Cryptography, Split- Key Management, Smart Contracts, Privacy-Preserving Cryp- tography, Institutional Crypto Custody
Kadhim Hayawi, Imran Makhdoom, Saifullah Khalid, Richard A. Ikuesan · 6 authors
Collaborative Intrusion Detection System (CIDS) protect large networks against distributed attacks. However, a CIDS is vulnerable to insider attacks that decrease the mutual trust among the CIDS nodes. Most existing trust management approaches rely on a central authority, trusted third parties or network peers for managing trust. The current techniques are prone to high false positives and vulnerable to various reputation attacks. For instance, device attestation manages trust among CIDS nodes by verifying the integrity of a node’s hardware and software configuration. However, it lacks real-time monitoring of the dynamic state, limiting its effectiveness against ongoing attacks and malware. Therefore, incorporating the system’s dynamic state in the trust framework is crucial, but it causes false positives requiring corrective mechanisms. To address these challenges, this paper proposes a blockchain-based integrated trust management framework for CIDS, incorporating the device’s genome attestation, the system’s dynamic parameters, and a false positive resilient reputation mechanism. By storing the reputation scores on the blockchain, the framework alleviates the need for a third party for trust management and thus mitigates attacks applicable to reputation-based systems. The paper performs a comprehensive security and performance analysis of the proposed framework to gauge its efficiency and study the effects of a penalty on a node’s reputation during the recovery and rally phases. We also study the impact of false positives on the reputation of a node. The results show that Hyperledger Fabric offers lower transaction latency and low CPU utilization compared to Ethereum Blockchain.
As IoT continues to expand, the security of connected devices remains a critical concern, particularly in the face of DDoS attacks. This study introduces a novel approach that leverages blockchain technology through smart contracts integrated with an advanced attack detection mechanism. Central to this approach is the Enhanced Residual Gated Recurrent Unit (ERGRU) architecture, designed to effectively identify and mitigate DDoS attacks within IoT networks. The Adaptive Coati Optimization Algorithm (ACOA) was used to adjust the hyperparameters of the ERGRU model, such as the learning rate and the number of GRU neurons, to further improve detection accuracy. In addition, the proposed framework uses a one-way compression function to generate secure hashes for input data, utilizing the Merkle-Damgård cryptography technique to ensure data integrity and confidentiality. The proposed solution was tested through a rigorous process using a DDoS dataset. Performance was assessed by focusing on metrics such as processing time, data integrity rate, and confidentially rate. The results demonstrate the effectiveness of the proposed smart contract-based framework in providing a durable and efficient protection mechanism against DDoS attacks in IoT environments.
Decentralized physical infrastructure networks (DePINs) are an emerging vertical within "Web3" replacing the traditional method that physical infrastructures are constructed. Yet, the boundaries between DePIN and traditional method of building crowd-sourced infrastructures such as citizen science initiatives or other Web3 verticals are not always so clear cut. In this work, we systematically analyze the differences between DePIN and other Web2 and Web3 verticals. For this, the study proposes a novel decision tree for classifying systems as DePIN. This tree is informed by prior studies and differentiates DePIN from related concepts using criteria such as the presence of a three-sided market, token-based incentives for supply, and the requirement for physical asset placement in those systems. The paper demonstrates the application of the decision tree to various blockchain systems, including Helium and Bitcoin, showcasing its practical utility in differentiating DePIN systems. This research offers significant contributions towards establishing a more objective and systematic approach to identifying and categorizing DePIN systems. It lays the groundwork for creating a comprehensive and unbiased database of DePIN systems, which will inform future research and development within this emerging sector.
Cognitive radio network is prone to many malicious attacks during the process of cooperative spectrum sensing. To overcome this limitation and to efficiently use the spectrum, we incorporate block chain technology in the Fusion center in order to eliminate the malicious users and to maintain only authorized sensing results in the ledger. Fusion center takes the responsibility of smart contract ownership and organizes spectrum sensing effectively. Any malicious attack found during sensing is identified using our proposed algorithm (DETMAL-Detection of malicious users algorithm). This paper proposes a noval algorithm ’DETMAL algorithm’, which is designed in such a way that it can handle both reliant strike and self reliant strike made by the attackers. Detected attackers who are sending falsified reports are removed from the honest local decisions to make an authorised global decision. Legitimate secondary users who are the participants of the smart contract are rewarded with incentives of ethers. DETMAL algorithm shows 12% increased detection probability at 0.1 false alarm probability than the existing algorithm. Results and simulations show that our proposed model outsmarts other existing models in terms of detection probability and accuracy.
In the realm of decentralized applications, smart contracts play a pivotal role in managing an extensive array of digital assets within blockchain networks. Ensuring the security of these digital assets hinges upon the adept detection of vulnerabilities present within smart contracts. Extensive research efforts have scrutinized and elucidated numerous smart contract vulnerabilities. However, certain vulnerabilities, including signature malleability, hash collision, and inconsequential code segments, remain relatively unexplored and devoid of dedicated detection tools. In response to this research gap, this paper addresses these three previously understudied vulnerabilities. We contribute to the field by creating a labeled dataset comprising vulnerable smart contracts. This dataset serves as a valuable resource for further scientific inquiries, enabling the testing and validation of various detection frameworks. Additionally, we present SmartSentry a static vulnerability detection framework capable of identifying these vulnerabilities. Using both dataflow and control flow analysis, our framework exhibits exceptional performance, successfully identifying labeled vulnerabilities and real-world vulnerabilities within production smart contracts with speed and efficiency. These efforts collectively enhance our understanding of smart contract vulnerabilities and contribute to the broader advancement of blockchain security.
The incorporation of blockchain technology into smart grid operations has attracted considerable attention owing to its decentralized, immutable, and transparent characteristics. This study examines the use of blockchain in the IEEE 14 bus system, illustrating its capacity to improve security, transparency, and resistance against cyber threats. The suggested system utilizes blockchain to guarantee safe data flow among nodes, including power generators, loads, and control centers. Every transaction related to power flow data, control signals, or market operations is recorded on an immutable ledger. The decentralized architecture of blockchain allows nodes to independently validate transactions through consensus processes such as Proof of Work (PoW) or Proof of Stake (PoS). This decentralization removes a singular point of failure, hence diminishing susceptibility to cyber-attacks aimed against centralized systems. Data integrity is safeguarded using cryptographic hash functions, which promptly identify any attempts to modify the stored information. The blockchain consensus system guarantees the recording of only valid transactions, hence enhancing security. Mathematical models for power flow analysis are incorporated into the blockchain framework, guaranteeing precise computations of voltage magnitudes, phase angles, and power transfers among buses. Simulations of the IEEE 14 bus system demonstrate enhanced system security and resilience, particularly against False Data Injection Attacks (FDIAs). The findings underscore how blockchain enhances overall reliability and mitigates the danger of unwanted data alteration. Blockchain enhances transparency and decentralization, providing a promising approach for guaranteeing the future of smart grid operations.
As the demand for cloud computing is increasing across the globe, more and more companies are shifting their data and IT infrastructure to the cloud. Log data integrity, security, and compliance are important in such an environment for anomaly detection, audit trails, and regulatory adherence. However, traditional log management systems suffer from tampering, data breaches, and centralized control. Recent research has, therefore, looked into blockchain technology as a means of boosting cloud security. This paper presents a blockchain-based approach to securing and authenticating AWS cloud logs. Utilizing the blockchain’s immutable and decentralized nature, the proposed system hashes AWS CloudWatch logs with a Python-based system and records the hash on the Ethereum blockchain via a smart contract. This process prevents undetected log alteration or deletion. The smart contract manages log hash storage and verification, with the Infura node facilitating Ethereum blockchain interactions and MetaMask handling private keys and test ETH. Original and modified log hashes are compared to assure the system’s efficiency, besides computing transaction costs and processing times. The findings suggest blockchain’s potential as a solid framework for log data security and compliance, offering a decentralized, tamper-proof log storage solution that enhances cloud infrastructure security and compliance for organizations.
Yunusa Ishaq, Ajuru Success Prince, Gbah Gonto Jean Claude, Togola Molobaly Di Bebe
Abstract: The transition to smart grids has revolutionized energy distribution, enabling more efficient and flexible power management through advanced communication and control systems. However, this interconnected structure makes smart grids vulnerable to cyberattacks, such as False Data Injection Attacks (FDIA), Distributed Denial of Service (DDoS) attacks, and data manipulation. These threats undermine the stability and reliability of the grid, and existing centralized security frameworks are often ill-equipped to address them due to their susceptibility to single points of failure and limited scalability. To overcome these challenges, this paper introduces a decentralized security framework that combines blockchain technology with machine learning (ML). The framework leverages blockchain to provide a transparent, immutable, and decentralized ledger, employing consensus mechanisms like Practical Byzantine Fault Tolerance (PBFT) or Proof of Authority (PoA) to ensure secure data validation. Alongside this, ML models, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), are used to detect anomalies in time-series data, such as FDIA, with high precision. Smart contracts embedded in the blockchain enable automated, real-time responses to threats, such as isolating compromised nodes or rerouting energy flows to maintain grid stability. Through simulations replicating real-world cyberattacks, the proposed framework demonstrated over 95% detection accuracy, a 30% reduction in response times, and enhanced computational efficiency with lower energy consumption. These results affirm the effectiveness of the framework as a scalable and resilient solution for modern smart grid security.
In light of the escalating complexity of the cyber threat environment, the role of Collaborative Intrusion Detection Systems (CIDSs) in reinforcing contemporary cybersecurity defenses is becoming ever more critical. This paper presents a Blockchain-based Collaborative Intrusion Detection Framework (BCIDF), an innovative methodology aimed at enhancing the efficacy of threat detection and information dissemination. To address the issue of alert collisions during data exchange, an Alternating Random Assignment Selection Mechanism (ARASM) is proposed. This mechanism aims to optimize the selection process of domain leader nodes, thereby partitioning traffic and reducing the size of conflict domains. Unlike conventional CIDS approaches that typically rely on independent node-level detection, our framework incorporates a Weighted Random Forest (WRF) ensemble learning algorithm, enabling collaborative detection among nodes and significantly boosting the system’s overall detection capability. The viability of the BCIDF framework has been rigorously assessed through extensive experimentation utilizing the NSL-KDD dataset. The empirical findings indicate that BCIDF outperforms traditional intrusion detection systems in terms of detection precision, offering a robust and highly effective solution within the realm of cybersecurity.
Abdullah Umar, Sumit Kumar Jha, Deepak Kumar, T. K. Ghose · 5 authors
In isolated microgrids, distributed energy resources (DERs) such as small-scale generators, energy storage systems, and flexible loads operate independently from the main grid. The challenge is to optimize these resources to minimize user costs while ensuring microgrid stability and efficiency. This paper presents an optimization framework for DERs, leveraging a game-theoretical approach to demand-side management (DSM) in an isolated microgrid environment. Each participant aims to minimize their total cost by strategically managing renewable energy generation, storage, and consumption. The framework models the DSM problem as a noncooperative game, identifying equilibrium points where no user can unilaterally reduce costs. The proximal decomposition algorithm is employed to iteratively update user strategies, ensuring convergence to a Nash equilibrium. Furthermore, a blockchain-based system with smart contracts is integrated to automate critical processes, including registration, event detection, DSM actions, and incentive distribution. This integration enhances transparency, security, and efficiency in the microgrid. During the registration phase, all devices are authenticated and authorized through a secure, transparent blockchain ledger. Event detection is managed by the microgrid Energy Management System (EMS), which continuously monitors voltage and frequency levels, triggering predefined smart contract responses to maintain stability. DSM actions are automatically executed by smart contracts, adjusting energy loads, generation, and storage to balance supply and demand dynamically. The smart contracts also manage the economic incentives that drive participant engagement. They calculate and distribute incentives based on predefined criteria, ensuring accurate and prompt allocation. This process is recorded on the blockchain, providing an immutable and auditable trail of actions and rewards. By leveraging blockchain technology and a game-theoretical approach, the proposed framework ensures continuous optimal operation despite fluctuations in energy demand and renewable generation. This dynamic and adaptive model promotes decentralized and efficient energy management within the microgrid, fostering a resilient and sustainable energy ecosystem.
Industry 4.0 has accelerated the adoption of the Industrial Internet of Things (IIoT), enabling intelligent communication among industrial devices, edge systems, and cloud platforms for smart manufacturing. However, conventional centralized security approaches are increasingly vulnerable to cyber threats, data tampering, and single-point failures. This paper proposes a secure blockchain-enabled IIoT architecture that integrates industrial sensing, edge computing, distributed ledger technology, cloud analytics, and intelligent decision-making. The framework employs device authentication, encrypted communication, decentralized consensus, smart contracts, and machine learning-based anomaly detection to enhance data integrity, secure information sharing, and cyber resilience. Experimental evaluation demonstrates improvements in communication security, authentication accuracy, transparency, scalability, latency, and throughput, making the proposed architecture a robust and scalable solution for secure next-generation smart engineering and industrial automation.
The integration of blockchain with Internet of Things (IoT) technologies has opened new avenues for secure, decentralized smart home automation. This paper presents a novel access-control framework using Binance Smart Chain (BSC) to ensure verifiable, low-latency device interaction without embedding consensus or token economics within the smart contract itself. The contract enforces permissions using application-level logic deployed on a public blockchain, enabling cost-efficient and tamper-proof control over IoT actuators. Functional testing via Foundry and transaction-level evaluation on BSC confirmed the system’s performance, with actuation delays averaging 3–4 seconds and costs consistently below $0.02 per interaction. Security validation through adversarial simulations demonstrated resistance against unauthorized access, replay attacks, and privilege misuse. Compared to Ethereum, Hyperledger Fabric, and IOTA, the proposed framework delivers a strong balance of scalability, affordability, and ease of deployment. The discussion explores future improvements, including role-based and attribute-based access control (RBAC and ABAC), sidechain integration, and zero-knowledge proofs to further optimize security, scalability, and user-centric automation. This work contributes a practical blueprint for secure, blockchain-driven smart home systems applicable to residential and enterprise IoT infrastructures.