Blockchain Papers

R. Bhandari, Neha Ramteke, Rajkumar Kankariya, Harshal Anil Salunkhe · 6 authors

No abstract is available for this record.

Taeyang Lee

No abstract is available for this record.

Priya Khune, Mayuresh Gulame, Komal Munde, Kanchan Wankhade · 5 authors

No abstract is available for this record.

A. M. A. Daiyan Kaif, Khandoker Shahjahan Alam, Sajal K. Das, Guo Chen · 6 authors

A novel architecture for smart meters in a Virtual Power Plant (VPP) is introduced in this research. By integrating blockchain technology, the system not only measures and quantifies diverse consumer data but also facilitates immediate control, hence improving demand responsiveness in a VPP environment. Mathematical models were created to optimize profit, battery reserve, and power balance. A novel transaction and security algorithm that enables peer-to-peer (P2P) transactions in a secure setting is used in conjunction with a power flow algorithm for real time monitoring and control to implement the proposed model. The lightweight characteristics of the algorithms enable faster and more effective computer processing. The unique identifier issued to each smart meter facilitates seamless integration with a blockchain smart contract, therefore enabling improved and secure P2P transactions. An innovative experimental setup demonstrated the framework’s ability to effectively manage energy flows while maintaining seamless wireless connection with the grid and executing transactions. The smart meter demonstrated exceptional efficiency in load management, resulting in an average loss of 1.9524W. A dedicated dapp was created just for this purpose. Through the strategic integration of algorithms and blockchain technology, this framework enhances the efficiency and reliability of the metering infrastructure, while also enabling secure transactions.

Ramesh Krishna Mahimalur

This paper introduces a novel framework for Zero-Knowledge Infrastructure Verification (ZKIV) that combines chaos engineering principles with security operations and zero-knowledge proofs to create a robust infrastructure verification system. By leveraging these technologies within a DevOps context, organizations can validate the integrity and security posture of their infrastructure without revealing sensitive configuration details or credentials. This approach, which we term ChaosSecOps, represents a significant advancement in infrastructure security verification, enabling teams to verify compliance, detect misconfigurations, and identify vulnerabilities without exposing sensitive information. Through a detailed AWS implementation case study, this paper demonstrates how ZKIV can be applied to modern cloud environments to enhance security, streamline compliance verification, and build resilient systems.Executive SummaryThis paper introduces Zero-Knowledge Infrastructure Verification (ZKIV), a novel framework for validating the security and compliance of complex, modern infrastructure (particularly cloud environments like AWS) without exposing sensitive configuration details or credentials. ZKIV achieves this by combining principles from:• Zero-Knowledge Proofs (ZKPs): While full cryptographic ZKPs are discussed, the paper focuses on "functional zero-knowledge" approaches practical for infrastructure. This means proving that security controls are in place and functioning correctly without revealing the underlying configurations themselves. Examples include black-box testing, output-only verification, and attestation.• Chaos Engineering: The deliberate introduction of controlled failures (like misconfigurations or simulated attacks) to test system resilience and the effectiveness of security controls.• Security Operations (SecOps): Continuous monitoring, threat response, and security automation practices.• DevOps: Leveraging automation, continuous integration/continuous delivery (CI/CD), and Infrastructure as Code (IaC). The integration of these disciplines is termed ChaosSecOps. Key Benefits of ZKIV• Enhanced Security: Verification happens without needing to expose sensitive data, reducing the attack surface.• Improved Compliance: Continuous, automated verification ensures ongoing adherence to regulatory and internal security policies (e.g., PCI DSS, SOC 2). Evidence is collected in a zero-knowledge manner.• Reduced Operation Risk: Proactive identification of vulnerabilities and misconfigurations before they can be exploited.• Increased Confidence: Greater assurance in the security posture due to systematic and continuous testing.• Scalability: Verification is automated and can be used across many systems.• Efficiency: Verification can be done faster.ZKIV Framework ComponentsThe framework consists of several key components that work together:• Verification Orchestrator: The central control point for scheduling, executing, and managing verification tests.• Policy Engine: Defines and enforces security and compliance rules (using policy-as-code).• Test Agents: Ephemeral (short-lived) components deployed within the infrastructure to perform black-box testing.• Evidence Collection System: Gathers test results in a way that preserves zero-knowledge (no sensitive data revealed).• Remediation Framework: Automates the fixing of identified security issues.AWS Implementation Case StudyA detailed case study demonstrates ZKIV implementation within a financial services organization using AWS. Key AWS services used include AWS Organizations, Security Hub, Lambda, Step Functions, EventBridge, Systems Manager, S3, and Config. The case study shows practical application of zero-knowledge techniques like:• Least-Privilege IAM Roles: Verification agents have only the permissions needed to check configurations, not to access the data they protect.• Output-Only Verification: Validating database security settings without querying the database itself.• Black-Box Network Testing: Using isolated containers to test network segmentation without accessing internal network configurations.

Asrar Mahboob, Muhammad Rashad, Ghulam Abbas, Zohaib Mushtaq · 6 authors

This paper presents a novel blockchain-embedded cybersecurity framework for industrial solar power systems, integrating immutable machine learning (ML) with distributed ledger technology. Our contribution focused on three fac... | Find, read and cite all the research you need on Tech Science Press

Lihua Zhang, Jiayi Bai, Yi Yang, Wenbiao Wang · 5 authors

No abstract is available for this record.

Gowrishankar Jayaraman, Ashok Kumar Munnangi, Ramesh Sekaran, Arunkumar Gopu · 5 authors

Industrial Cyber-Physical Systems (ICPSs) play a vital role in modern industries by providing an intellectual foundation for automated operations. With the increasing integration of information-driven processes, ensuring the ... | Find, read and cite all the research you need on Tech Science Press

Saba Makasarashvili

This paper studies the economic feasibility of coordinated majority attacks on the Ethereum blockchain under its Proof-of-Stake consensus mechanism. Focusing on Ethereum’s validator-based finality rules, the paper models a corruptive attack in which validators are induced to deviate from the protocol in exchange for off-chain transfers. Under perfect information, the model derives a bribery cost schedule implying that an attacker must effectively finance just over half of total staked ether to assemble two-thirds of finality power, despite only needing to corrupt a subset of validators at each margin. The paper further shows that, under risk-neutral Bayesian behavior, uncertainty about the attacker’s stake weakly reduces expected bribery costs due to the concavity of the compensation function. The results highlight how Ethereum’s economic security depends critically on slashing design, information structure, and validator fragmentation.

Amelia Grace, И В Ковалев, Yu.A. Sosnina

A mathematical approach is proposed for evaluating the resilience of a distributed ledger of SmartDust-class sensor microsystems under conditions of cyber-physical attacks and industrial interference. A stochastic model of the trusted sensor network is developed, incorporating the probabilities of node compromise, telemetry packet loss, and data verification errors. Analytical expressions are derived for the indicators of data trustworthiness, latency, and energy reliability of the system. The simulation results demonstrate the influence of network topology parameters and attack intensity on the integral resilience index of the sensor infrastructure.

Bhupendra sahu

No abstract is available for this record.

Usman Khalil, Mueen Uddin, Hashem Alaidaros, Adnan Akhunzada

Abstract In the rapidly evolving landscape of IoT-enabled smart devices, significant challenges persist in integration to web3, security, and data reliability. This research presents the design and integration of IoT assets, particularly devices, through the Novel Decentralized Smart City of Things (DSCoT) framework. ESP32 microcontrollers serve as Ethereum clients, generating Externally Owned Accounts (EOA) for device identification and authentication. Despite resource constraints, including limited computational capabilities, essential libraries that manage tasks such as Wi-Fi module control, interaction with Ethereum-based blockchains, TCP connection management, and EEPROM operations for persistent data storage. The code is structured with functions for Wi-Fi setup, TCP API requests, and secure communication challenges. Integration involves compiling and flashing the code onto ESP32 devices, verifying EOA generation, and mapping devices, fog nodes, and users through smart contract interactions. The deployment process culminates in the generation of Non-Fungible Tokens (NFTs) for user authentication, with transaction verification on the Goerli testnet confirming successful DSCoT edge system implementation. This research underscores the importance of secure and decentralized integration of IoT-enabled smart devices to the blockchain, enhancing performance while ensuring security and transparency.

Sergio Anguita, José Miguel-Alonso, Aitor Gómez-Goiri

No abstract is available for this record.

Mohammad Amin M. Shabestari, Mohammad Sobhan Sakhaei, Payman Rezaei, Amirhossein Nikoofard

With rapid rise of cryptocurrencies, integrating blockchain into electrical networks has become inevitable. This has led to a notable increase in mining within microgrids, introducing a new type of electrical load. These farms introduce new challenges, such as increasing peak loads, demand fluctuations, and reducing grid reliability. This paper presents a novel framework for managing cryptocurrency mining farms as flexible loads within power grids. A two-stage energy management framework is proposed: the first stage involves day-ahead scheduling to optimize operation cost, mining farm revenue, and reliability, while the second stage provides real-time adjustments every 15 minutes to minimize fluctuation cost. The results demonstrate that the proposed model reduces operation costs by 23.5% compared to unregulated mining loads, while also increasing social welfare by 12.7%. Furthermore, a 40% increase in mining demand leads to a 4.6% improvement in reliability compared to the baseline scenario, emphasizing the effectiveness of the proposed framework.

Chong Shao, Xumin Liu, Ding Li, Xiaoting Chen

This study presents a distributed electricity trading system using smart contracts to improve transaction efficiency and reduce costs in power markets. Three trading models are analyzed: centralized trading, blockchain-based decentralized trading, and smart contract-driven automated trading. The advantages and challenges of each model are examined, focusing on factors like node inclusion time, transaction costs, and price stability. The results show that the smart contract-driven model outperforms the others by increasing market efficiency, lowering transaction costs, and reducing price fluctuations. Through simulations and real-world analysis, this study provides support for using blockchain technology in power markets and offers practical advice for improving electricity trading systems. The findings suggest that the proposed system could greatly enhance transparency, efficiency, and cost-effectiveness in distributed energy markets, even in uncertain market conditions.

Amit Kumar Vishwakarma, Pratyush Kumar Patro, Adolf Acquaye, Raja Jayaraman · 5 authors

In attempts to progress the transition towards decarbonizing energy systems and achieving sustainable development goals linked to energy, renewable energy sources (RES), which are decentrally deployed, are being widely promoted. However, RES are constrained by many barriers, some of which are technical in nature, such as energy losses. For instance, traditional peer-to-peer (P2P) trading systems have been used as a mechanism to advance RES because they offer a promising solution for decentralized energy trading without third-party intermediaries. However, it faces significant challenges, notably in accounting for energy losses during transmission and distribution, thus reducing overall system efficiency. We propose an end-to-end blockchain-based solution to provide traceability of energy loss in a distributed network and enhance transaction security, trust, and operational efficiency by linking prosumers and consumers. Our solution uses smart contracts to automate business transactions among the stakeholders. We develop six algorithms and deploy the smart contracts to demonstrate the successful deployment of the P2P energy trading process. Our solution effectively provides traceability of energy loss and reliably secured P2P energy transaction verification. We also present the cost and security analysis to demonstrate the affordability and reliability of our solution. We make our smart contract codes publicly available on GitHub.

Alaa Awad Abdellatif, Khaled Shaban, Ahmed Massoud

This study introduces a secure, adaptable, and decentralized learning framework empowered by blockchain technology to enhance smart grid security and efficiency. Security is achieved through blockchain’s ledger, ensuring data integrity, privacy, and resilience. Adaptability refers to the framework’s ability to adjust to changing conditions, supporting multiple learning paradigms . Decentralization enhances fault tolerance by distributing control across nodes. Our framework excels in scalability, data-exchange security, and rapid response times , aiming to establish an intelligent blockchain-based smart grid supporting centralized learning (CL), federated learning (FL), and active federated learning (AFL). We present an innovative blockchain-based architecture customized to optimize information sharing and security within the blockchain. Our solution addresses various learning paradigm requirements by: (i) Selecting reliable entities for participation based on high-quality training data models; (ii) Acquiring a reliable subset of data for CL and AFL, balancing learning performance , latency, and cost; (iii) Adjusting blockchain configuration to align with specific learning paradigm requirements. Results from real-world datasets demonstrate superior performance compared to existing solutions. Our framework achieves high learning performance while minimizing latency and blockchain costs.

Md. Mainul Islam, Rachad Atat, Muhammad Ismail, Katherine Davis · 5 authors

Abstract Enhancing the resilience and reliability of power grids is crucial amid rising cyber threats and system complexities. To address these challenges, this paper proposes an energy‐efficient, consortium blockchain‐based global alarm system for power grid management. Using smart contracts and the proof of‐authority consensus algorithm, the alarm system triggers global alarms upon detecting local anomalies, ensuring a prompt response to partition the power grid and mitigate failures. The effectiveness is validated by simulating the Iberian power system with 15 providers from various regions. Key metrics, such as load shedding, damage reduction, energy consumption, latency, and transaction costs, are used to assess the performance. Through simulations, we show that the blockchain‐based system effectively limits the damage propagation and the load shedding during cascading failures by delaying the onset of instability and maintaining lower damage levels compared to non‐blockchain scenarios. Our investigations reveal that the proposed global alarm mechanism reduces the damage and load shedding by up to 29% and 87%, respectively, showcasing its potential for preventing widespread outages.

Aswani Devi Aguru, Amrit Pandey, Suresh Babu Erukala, Ali Kashif Bashir · 7 authors

Routing protocol for low-power and lossy network (RPL) is a routing protocol for resource-constrained Internet of Things (IoT) network devices. RPL has become a widely adopted protocol for routing in low-powered device networks. However, it lacks essential security features, including end-to-end security, robust authentication, and intrusion detection capabilities. Blockchain is a decentralized and immutable digital ledger that records transactions across multiple computers. It provides privacy, transparency, security, and trust. In this work, we proposed a blockchain-based reliable RPL protocol called reliable-RPL, which uses node reliability, link reliability, and relative trust scores of RPL-enabled IoT devices. The parent selection and network topology formulation are based on the proposed reliability-aware objective function. A lightweight ECC-based scheme performs registration, identification, and authentication of RPL-enabled IoT devices. The consistent topological updates from these authenticated IoT devices are used to secure routing paths in RPL-enabled networks. Using a modified trickle algorithm, we employed a reputation-based trust system that monitors and labels malicious nodes based on their reliable activities. The novelty of the proposed framework relies on integrating Contiki-NG (as fronted for IoT network simulation) and Hyperledger Fabric (as a backend for blockchain-based device authentication and trust-based attack resilience regarding rank, replay, sinkhole, and route poisoning attacks). The experimental evaluation of reliable-RPL has demonstrated its effectiveness compared to state-of-the-art methods regarding significant performance metrics, including packet loss, routing overhead, and throughput on Hyperledger Caliper.

Ohood Alharbi, Riaz Ahmed Shaikh, Rameez Asif

Intrusion Detection Systems (IDS) are the key for securing the rapidly evolving Internet-of-Things (IoT), where data security and privacy will become increasingly important in the forthcoming era. This research presents an innovative method for improving IDS performance through the integration of Artificial Intelligence (AI), Blockchain, and Digital Twin (DT) technologies. AI is utilized for real-time anomaly detection, whereas DT replicate device behavior for predicting threats and Blockchain ensures secure, decentralized data transmission. Energy-efficient zero-knowledge proofs are employed to meet the energy requirements of Blockchain, enhancing both security and resource efficiency. The performance of the suggested system will be assessed based on detection accuracy, latency, scalability, energy efficiency, and privacy preservation. This distinctive integration of advanced technologies delivers a multi-faceted security system, providing a thorough respond to for strengthening security in IoT networks.

Jin Qian, Jun Luo, Liquan Chen, Bangwei Yin · 6 authors

The authentication of identities within the smart grid system is crucial for ensuring its security and stable operation. With the emergence of smart grid technology, the significance of identity authentication in smart grid systems has become increasingly evident. Traditional authentication techniques, such as Direct Anonymous Attestation (DAA) based on RSA or ECC algorithm, face threats from quantum computing. On the other hand, lattice-based cryptography utilizes lattice structures and difficult problems to ensure the security and reliability of authentication against quantum computing threats, leading to the development of various Lattice-based Direct Anonymous Attestation (LDAA) protocols. In traditional LDAA schemes, the signature process involves using issued identity certificates to ensure trustworthiness. To maintain identity anonymity and trust, this process usually requires an additional commitment scheme and the use of large coefficient expressions for zero-knowledge proofs. This paper presents a single-domain LDAA scheme based on lattice cryptography. It significantly enhances authentication efficiency and performance by employing an innovative multiplication relations proof mechanism in lattices. Comparative experimental results validate the superiority of the proposed scheme.

Dipabali Nath, Parama Bhaumik

Managing blood screening data is critical for healthcare, requiring secure and transparent systems to safeguard public health. Existing methods often lack secure access to donors' medical histories, posing risks. Hyperledger Fabric, a distributed ledger technology, offers a robust solution for managing health data securely. This paper proposes a decentralized blood screening system using Hyperledger Fabric to record transactions, donor, and recipient data, employing chaincode for eligibility checks. Its modular architecture ensures privacy, integrity, and immutability. The study explores how Hyperledger Fabric's architecture meets blood screening requirements, focusing on optimizing latency, throughput, and block space utilization through customized configurations. Experiments with Hyperledger Fabric v2.5 assessed performance by adjusting endorsement strategies and block sizes. The results, analyzed for performance differences, provide recommendations for tuning configuration factors like batch timeout and maximum message count. The Hyperledger Caliper tool was used to measure transaction throughput and latency, leading to optimized blockchain architecture for blood screening. Findings indicate that customized blockchain configurations can significantly enhance performance metrics crucial for real-time healthcare applications.

Sam Goundar

The rapid evolution of cyber threats, driven by artificial intelligence (AI) and machine learning (ML), has exposed critical gaps in traditional cybersecurity frameworks, particularly in real-time threat detection and response. This paper presents research on the Next-Gen Cyber Security Sentinel, a system that integrates AI algorithms with blockchain-based smart contracts to detect and mitigate sophisticated, AI-powered cyberattacks in real-time. Through the use of Software-Defined Networks (SDN), the system simulates complex attack scenarios, providing flexible and scalable network management. Penetration testing confirmed the system's ability to detect, respond to, and mitigate advanced cyber threats, ensuring enhanced protection of critical infrastructure. The findings were both novel and innovative, demonstrating that the integration of AI and blockchain technology significantly improves the speed and accuracy of real-time threat detection. This research contributes to the field of cybersecurity by offering a robust, scalable solution to counter emerging AI-driven attacks, particularly in critical sectors such as smart cities and Industry 4.0 environments. These findings offer crucial insights for advancing cybersecurity solutions in the face of rapidly evolving, AI-driven cyber threats. The AI-Blockchain platform achieved a 95% detection rate with a 2% false positive rate and maintained blockchain transaction latency under 200 milliseconds, demonstrating significant improvements in real-time threat detection and response capabilities.

Huijiong Yang, Bin Xie, Jianhuan Wang, Guyue Li · 5 authors

Decentralized identity (DID) is pivotal to Web3 applications as it empowers users to manage their identities and credentials without relying on any central authority. Multi-controller is a new and indispensable scenario outlined by the W3C DID standards, while its privacy and security issues have not yet been fully explored. In this paper, we find two new attacks caused by multiple controllers toward DID management, and propose a privacy-preserving and secure identity management scheme to defend against both attacks. The first proposed controller-correlation attack allows an attacker to infer relationships between different subjects by correlating the public keys uploaded by multiple controllers to the blockchain. To avoid this kind of privacy leakage, we propose a masking scheme based on the Merkle tree, which allows the controllers to prove their ownership over the multi-controller identities without publicizing the plaintext of their public keys. The other identity impersonation attack exploits insecure controller revocation caused by high block synchronization latency. To resist this attack, we propose a lightweight authentication scheme. The holders provide digest freshness proof while the verifiers only need to download block headers. To evaluate the feasibility of our proposed scheme, we implement our system on the Sepolia TestNet. The experimental result demonstrates that our system can prevent these attacks with acceptable gas consumption and time consumption, compared with the state-of-the-art.