This paper investigates the temporal evolution of cryptocurrency time series using information measures such as complexity, entropy, and Fisher information. The main objective is to differentiate between various levels of randomness and chaos. The methodology was applied to 176 daily closing price time series of different cryptocurrencies, from October 2015 to October 2024, with more than 30 days of data and not completely null. Complexity–entropy causality plane (CECP) analysis reveals that daily cryptocurrency series with lengths of two years or less exhibit chaotic behavior, while those longer than two years display stochastic behavior. Most longer series resemble colored noise, with the parameter k varying between 0 and 2. Additionally, Natural Language Processing (NLP) analysis identified the most relevant terms in each white paper, facilitating a clustering method that resulted in four distinct clusters. However, no significant characteristics were found across these clusters in terms of the dynamics of the time series. This finding challenges the assumption that project narratives dictate market behavior. For this reason, investment recommendations should prioritize real-time informational metrics over whitepaper content.
Blockchain technology has emerged as a transformative innovation, providing a transparent, immutable, and decentralized platform that underpins critical applications across industries such as cryptocurrencies, supply chain management, healthcare, and finance. Despite their promise of enhanced security and trust, the increasing sophistication of cyberattacks has exposed vulnerabilities within blockchain ecosystems, posing severe threats to their integrity, reliability, and adoption. This study presents a comprehensive and systematic review of blockchain vulnerabilities by categorizing and analyzing potential threats, including network-level attacks, consensus-based exploits, smart contract vulnerabilities, and user-centric risks. Furthermore, the research evaluates existing countermeasures and mitigation strategies by examining their effectiveness, scalability, and adaptability to diverse blockchain architectures and use cases. The study highlights the critical need for context-aware security solutions that address the unique requirements of various blockchain applications and proposes a framework for advancing proactive and resilient security designs. By bridging gaps in the existing literature, this research offers valuable insights for academics, industry practitioners, and policymakers, contributing to the ongoing development of robust and secure decentralized ecosystems.
Ethereum’s scalability has been a major concern due to its limited transaction throughput and high fees. To address these limitations, Polygon has emerged as a sidechain solution that facilitates asset transfers between Ethereum and Polygon, thereby improving scalability and reducing costs. However, current cross-chain transactions, particularly those between Ethereum and Polygon, lack transparency and traceability. This paper proposes a method to track cross-chain transactions across EVM-compatible blockchains. It leverages the unique feature that user addresses are consistent across EVM-compatible blockchains. We develop a matching heuristic algorithm that links transactions between the source and target chains by combining transaction time, value, and token identification. Applying our methodology to over 2 million cross-chain transactions (August 2020–August 2023) between Ethereum and Polygon, we achieve matching rates of up to 99.65% for deposits and 92.78% for withdrawals, across different asset types including Ether, ERC-20 tokens, and NFTs. In addition, we provide a comprehensive analysis of various properties and characteristics of cross-chain transactions. Our methodology and findings contribute to a better understanding of cross-chain transaction dynamics and bridge performance, with implications for improving bridge efficiency and security in cross-chain operations.
The rapid evolution of Decentralized Finance (DeFi) has introduced innovative financial services, offering accessibility, efficiency, and transparency. However, the integration of DeFi into global capital markets presents systemic risks, including liquidity shocks, smart contract vulnerabilities, and regulatory arbitrage. This review explores the intersection of DeFi protocols with systemic risk frameworks to enhance capital market stability and regulatory oversight. By analyzing risk assessment methodologies, stress-testing mechanisms, and governance models, the study highlights strategies for mitigating financial contagion and ensuring market resilience. Furthermore, it examines regulatory approaches, such as real-time compliance monitoring and cross-border coordination, to bridge the gap between decentralized ecosystems and traditional financial regulations. Through case studies and empirical data, this paper underscores the importance of integrating robust risk frameworks with DeFi innovations to foster sustainable financial markets. The findings contribute to ongoing discussions on balancing financial innovation with risk management, providing insights for policymakers, regulators, and industry stakeholders navigating the evolving landscape of digital finance.
Traditional electronic voting systems face sig-nificant challenges, including susceptibility to tam-pering, lack of transparency, and vulnerabilities in voter authentication.To address these issues, this paper proposes a decentralized e-voting archi-tecture that integrates Aadhaar-based identity val-idation, biometric authentication (fingerprint and facial recognition), and Ethereum blockchain tech-nology for secure and immutable vote recording [1].The system leverages multi-factor authentica-tion to ensure only eligible voters can participate, while blockchain's distributed ledger guarantees tamper-proof storage and real-time auditability of votes.Experimental evaluations demonstrate that the proposed framework achieves a throughput of over 10,000 transactions per second with 99.99% uptime, making it scalable for large-scale elections.By eliminating centralized points of failure and enabling remote voting, this approach signif-icantly enhances electoral integrity, accessibility, and public trust.Future work will explore inte-gration with postquantum cryptography to further strengthen long-term security.
Open access
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
TetraUnified v2.0 presents a fully revised, academically aligned research framework integrating three experimental components: Tetrahedral Key Exchange (TKE):Exploratory key exchange mechanism based on recursive geometric projections. Recursive Tesseract Hashing (RTH):Hyperdimensional hashing model using 16-axis Clifford projections and recursive entropy mixing. Quantum Isoca-Dodecahedral Lattice Encryption (QIDL):Conceptual encoding model for representing plaintext within dynamic polyhedral phase lattices. This version restructures the system into a coherent research-grade framework, emphasizing mathematical clarity, reproducibility, consistent notation, and proper cryptographic disclaimers.No security guarantees are claimed and no component should be used in production systems.All structures are intended strictly for experimental simulation, prototyping, and conceptual evaluation. Purpose of This Release Version 2.0 was developed to achieve three objectives: Remove speculative, metaphorical, or narrative content from earlier drafts and establish a formal academic tone. Strengthen mathematical structure and notation, including explicit operator definitions and theorem–proof formulations. Position the system as a technical R&D testbed, rather than a security product or operational cryptographic protocol. This release supersedes all previous versions.Earlier manuscripts are preserved only as historical development notes. Key Improvements in v2.0 1. Formal Mathematical Structures Includes new theorem–proof style sections addressing: TKE reconstruction consistency RTH entropy evolution under recursion QIDL transformation intractability (as a conceptual model) Defined core operators: Projection (𝒯) Modulation (f) Reconstruction () Polyhedral rotation (_{I,D}) Sealing (𝒮) Geometric embeddings now use clearly stated synthetic Clifford bases. 2. Cryptographic Positioning TKE, RTH, and QIDL are explicitly described as experimental, unverified, not secure, and not production-ready. No hardness assumptions are claimed. All constructs are positioned as alternative simulation models inspired by geometric/topological methods. 3. Distributed Systems & Navigation Concepts Introduces a conceptual framework for: phase-based synchronization inertial alignment without external timing sources distributed state coordination under high latency resilience to environmental drift or partial network partitions 4. Comparison with Existing Quantum Programming Includes a revised comparison table contrasting: NISQ-era quantum programming TetraUnified’s hyperdimensional simulation models Highlights key architectural differences without implying superiority. 5. Expanded Application Sections Updated application discussions for TKE, RTH, and QIDL covering: distributed identity experiments mesh communication models ledger integrity prototyping inertial navigation research off-world / high-latency environments multi-agent swarm coordination recursive lineage tracking for AI pods All applications are strictly conceptual research pathways, not operational deployments. Version Philosophy TetraUnified v2.0 establishes the framework as: an academic-style experimental cryptography model a research environment for hyperdimensional and geometric transformations an R&D prototype for studying non-linear distributed coordination a computational sandbox for exploring alternative post-quantum architectures No practical security, correctness, or adversarial resistance should be inferred.Formal verification and cryptanalysis remain open areas for future work. Included Artifacts This release includes: the revised LaTeX manuscript (PDF) updated mathematical definitions for TKE, RTH, QIDL reference diagrams and basis definitions example code structures (if present in repository) reproducibility metadata and version history Notes on Previous Versions Earlier versions contained exploratory and speculative material.Version 2.0 replaces these with a rigorous mathematical and systems-engineering structure. Per Zenodo policies, earlier versions remain visible but represent developmental prototypes only.The DOI series now resolves to v2.0 as the authoritative technical edition. Intended Use TetraUnified v2.0 is intended for: researchers exploring geometric or topological cryptography models distributed systems experimentation verifiable computation and XR/digital-twin state modeling conceptual post-quantum architecture studies academic and peer review simulation, prototyping, and reproducibility analysis This work is not intended for operational cryptography, production deployment, or security-critical environments. Citation MacDonald, M. (2025).TetraUnified v2.0 — Experimental Framework for Hyperdimensional Cryptography, Recursive Hashing, and Distributed State Models.Zenodo. https://doi.org/10.5281/zenodo.17759222
Proshanta Kumar Bhowmik, Faiaz Rahat Chowdhury, Md Sumsuzzaman, Rejon Kumar Ray · 9 authors
Cryptocurrency markets in the USA, especially that of Bitcoin, are plagued by extreme volatility fueled by a dynamic intersection of macroeconomic forces, speculator behavior, and sentiment of investors. Conventional financial models cannot keep pace with the high-frequency price changes typical of digital assets, prompting the need for novel methodologies that can better account for unstructured data like social media sentiment, news reports, and discussion forum postings. The central aim of this study was to establish a strong model that integrates sentiment analysis and machine learning methods to forecast the price movements of Bitcoin. The dataset used included multi-source sentiment data and cryptocurrency market indicators, which allow for in-depth analysis of public emotion on cryptocurrency volatility. Sentiment was sourced from Twitter (tweet text with Bitcoin hashtags and keyword mentions), Reddit (r/Bitcoin and r/Crypto Currency subreddits), and financial headlines (Bloomberg, CoinDesk, Reuters), covering the timeframe of 2019–2024 to ensure the inclusion of various market cycles. Textual data was pre-cleaned to remove noise signals (bots, spam, non-English text) and annotated for sentiment polarity (positive, negative, neutral) using both VADER (Valence Aware Dictionary for sEntiment Reasoner) and fine-tuned BERT models for contextual relevance. In analyzing how sentiment affects the volatility of the Bitcoin market, we used various modeling methods such as Logistic Regression, Random Forest Classifier, and Support Vector Machines. Support Vector Machines stands slightly ahead in terms of accuracy, implying that it might be the strongest among the three for this particular task. Logistic Regression and Random Forest both show similar levels of accuracy, which means that both of them are also strong, though less optimal compared to the Random Forest model. The use of sentiment analysis in financial markets, especially in the cryptocurrency market, provides U.S.-based investors and traders with a valuable means of risk protection. Through the use of sentiment-aware forecasts, investors can make predictions of market trends and probable price movements based on public sentiment. Crypto-fintech platforms can leverage sentiment analysis to build real-time alert systems that update users on important market movements. Through social media and news channels, the platforms can issue alerts on impending price volatility or impending trends, allowing the user to react quickly to market forces. The capability to bring in real-time social media APIs for live predictions marks a critical leap for sentiment analysis in the cryptocurrency market. Through APIs like Twitter, Reddit, and other social media platforms, investors can get instant readings on public sentiment, which in turn will allow them to make real-time and better-informed trading decisions.
Bitcoin blockchain uses hash-based Proof-of-Work (PoW) that prevents unwanted participants from hogging the network resources. Anyone entering the mining game has to prove that they have expended a specific amount of computational power. However, the most popular Bitcoin blockchain consumes 175.87 TWh of electrical energy annually, and most of this energy is wasted on hash calculations, which serve no additional purpose. Several studies have explored re-purposing the wasted energy by replacing the hash function with meaningful computational problems that have practical applications. Minimum Dominating Set (MDS) in networks has numerous real-life applications. Building on this concept, Chrisimos [TrustCom '23] was proposed to replace hash-based PoW with the computation of a dominating set on real-life graph instances. However, Chrisimos has several drawbacks regarding efficiency and solution quality. This work presents a new framework for Useful PoW, ScaloWork, that decides the block proposer for the Bitcoin blockchain based on the solution for the dominating set problem. ScaloWork relies on the property of graph isomorphism and guarantees solution extractability. We also propose a distributed approach for calculating the dominating set, allowing miners to collaborate in a pool. This enables ScaloWork to handle larger graphs relevant to real-life applications, thereby enhancing scalability. Our framework also eliminates the problem of free-riders, ensuring fairness in the distribution of block rewards. We perform a detailed security analysis of our framework and prove our scheme as secure as hash-based PoW. We implement a prototype of our framework, and the results show that our system outperforms Chrisimos in all aspects.
Blockchain technology emerges as a transformative solution for healthcare data security and management challenges. This comprehensive article focuses on how blockchain revolutionizes healthcare information exchange through distributed ledger technology, smart contracts, and automated administrative processes. The technology offers enhanced security measures, improved patient data control, and streamlined operations across healthcare institutions. Key considerations include implementation challenges such as scalability requirements, regulatory compliance needs, and integration with existing systems. The adoption of blockchain in healthcare demonstrates significant potential for reducing data breaches, optimizing administrative efficiency, and enabling secure real-time information sharing while maintaining patient privacy and data integrity.
In the modern financial landscape, cryptocurrency investments have gained substantial traction among both seasoned and novice investors. However, given the complexity, volatility, and risk associated with digital currencies, financial literacy plays a fundamental role in shaping an individual’s investment decisions. This study explores the intricate relationship between financial literacy and cryptocurrency investment behavior, analyzing how knowledge of financial principles influences an investor’s ability to assess risk, formulate strategies, and make informed decisions in the highly speculative crypto market. This research adopts a mixed-methods approach, combining both qualitative and quantitative data collection techniques. Surveys and structured interviews were conducted among cryptocurrency investors of various demographics, ranging from experienced market participants to first-time investors, to assess their understanding of financial concepts and their influence on investment strategies. Additionally, secondary data was sourced from financial reports, academic journals, and regulatory analyses to contextualize the findings within broader financial literacy frameworks. The results of the study indicate that individuals with a higher level of financial literacy are more likely to engage in thorough research before investing, effectively utilize risk management techniques, and demonstrate a more disciplined approach to cryptocurrency trading. Conversely, a subset of investors, despite having adequate financial knowledge, continues to engage in speculative trading driven by social trends, herd mentality, and market hype, often leading to irrational financial decisions. This suggests that while financial literacy is crucial, external factors such as psychological influences, peer recommendations, and media narratives can significantly impact investment behavior. The study further highlights the role of financial education in mitigating impulsive investment decisions. It emphasizes the need for targeted educational programs that equip investors with the analytical skills required to navigate the complexities of digital asset investments. By understanding key financial concepts such as market volatility, asset diversification, and risk assessment, investors can make more informed decisions and minimize exposure to financial losses. In conclusion, this study provides valuable insights into the role of financial literacy in shaping investment behaviors in the cryptocurrency space. The findings contribute to the ongoing discussion on financial education and its implications for emerging markets, digital assets, and investment decision-making processes. The study also serves as a foundation for further research on how investor psychology, regulatory frameworks, and technological advancements intersect with financial literacy in the evolving cryptocurrency ecosystem.
Hendri Dewarto Silitonga, Ratna Artha Windari, Si Ngurah Ardhya
Penelitian ini bertujuan untuk (1) menganalisis dan memahami pengaturan hukum mengenai transaksi aset digital cryptocurrency di indonesia dalam Undang- Undang Informasi dan Transaksi Elektronik sebagai acuan dalam memberikan kepastian hukum terhadap penggunaan aset digital cryptocurrency di Indonesia, (2) mengidentifikasi keabsahan smart contract pada platform Ethereum berdasarkan sistem hukum kontrak di Indonesia berdasarkan perspektif syarat sahnya perjanjian yang dimuat di dalam pasal 1320 KUH Perdata. Jenis penelitian yang digunakan dalam penelitian ini adalah yuridis normatif , yakni melalui pendekatan perundang-undangan (statute approach) dan pendekatan perbandingan (comparative approach) . Sumber bahan hukum yang digunakan yaitu KUH Perdata, UU ITE, Bappebti, PP, dan artikel ilmiah yang relevan. Hasil penelitian menunjukkan bahwa (1) Undang-Undang Informasi dan Transaksi Elektronik tidak mencamtumkan secara eksplisit mengenai transaksi aset digital, istilah dokumen elektronik dan informasi elektronik, menkategorikan aset digital yang menjadikan aset digital memiliki kedudukan yang sah dan diakui secara hukum. (2) Keabsahan smart contract pada platform Ethereum jelas tidak memenuhi syarat subjektif dalam 1320 KUH Perdata yang membuat smart contract batal atau tidak sah sebagai suatu perjanjian yang mengikat para pihak.
The migration of E-commerce applications to Blockchain offers a resilient solution to the vulnerabilities inherent in centralized servers. By dispersing data across multiple nodes, Blockchain ensures continuous service availability, even in the event of server failure or cyber attacks. Moreover, its inherent encryption and immutability features guarantee the security and integrity of customer and product data. Blockchain revolutionizes the E-commerce landscape by providing decentralized platforms that address critical challenges such as security, transparency, efficiency, and trust. This technology presents numerous opportunities for enhancing various aspects of E-commerce, including payment systems, supply chain management, and the implementation of smart contracts for automated workflows. With its robust capabilities, Blockchain emerges as a pivotal development poised to transform the E- commerce industry, paving the way for enhanced security, transparency, and efficiency in online transactions.
Ethereum, the leading platform for decentralized applications, faces challenges in maintaining decentralization due to the significant hardware requirements for validators to store Ethereum's entire state. To address this, the concept of stateless clients is under exploration, enabling validators to verify transactions using cryptographic witnesses rather than the full state. This paper compares two approaches currently being discussed for achieving statelessness: Verkle trees utilizing vector commitments and binary Merkle trees combined with SNARKs. Benchmarks are performed to evaluate proving time, witness size, and verification time. The results reveal that the Verkle tree implementation used for benchmarking offers proving and verification times on the order of seconds and proof sizes on the order of one MB. The SNARK-based Merkle trees exhibit slow proof generation times, while offering constant and fast verification time. Overall, the results indicate for Verkle trees to provide a more practical solution for Ethereum's stateless future, but both methods offer valuable insights into reducing the state burden on Ethereum nodes. We make the code used for benchmarking available on GitHub.
Some of our current public key methods use a trap door to implement digital signature methods. This includes the RSA method, which uses Fermat's little theorem to support the creation and verification of a digital signature. The problem with a back-door is that the actual trap-door method could, in the end, be discovered. With the rise of PQC (Post Quantum Cryptography), we will see a range of methods that will not use trap doors and provide stronger proof of security. In this case, we use hash-based signatures (as used with SPHINCS+) and Fiat Shamir signatures using Zero Knowledge Proofs (as used with Dilithium).
The Web3 ecosystem, underpinned by cryptographic primitives and decentralized consensus, represents a high-stakes environment where software vulnerabilities and incentive misalignments translate directly into financial loss. As Large Language Models (LLMs) are increasingly integrated into this domain for tasks ranging from smart contract auditing to decentralized finance analytics, ensuring their reliability is paramount. However, general-purpose benchmarks fail to capture the specialized reasoning required for these adversarial and protocol-driven settings. To bridge this gap, we introduce DMind Benchmark, a comprehensive evaluation suite designed to rigorously assess LLM proficiency across the Web3 stack. DMind Benchmark encompasses nine distinct subdomains (spanning infrastructure, smart contracts, token economics, etc.) and combines objective knowledge retrieval with complex open-ended reasoning tasks that emulate real-world operational challenges. We conduct an extensive evaluation of 31 leading proprietary and open-weights models, employing a contamination-aware pipeline and verifying the statistical robustness of our scoring protocol through rigorous cross-judge consistency checks. Our analysis reveals a critical dichotomy: while models demonstrate competence in foundational infrastructure concepts, they exhibit significant vulnerabilities in high-reasoning tasks such as security auditing. Furthermore, we provide a Pareto analysis to guide cost-effective deployment and demonstrate through adversarial experiments that high performance on DMind Benchmark necessitates genuine reasoning rather than superficial memorization. Since its open-source release in April 2025, DMind Benchmark achieved the #1 trending position on Hugging Face for nearly a week and accumulated over 13k downloads by June 2026, establishing itself as a standard for advancing secure and trustworthy AI in Web3.
Dankrad Feist, Gottfried Herold, Mark Simkin, Benedikt Wagner
Data Availability Sampling (DAS), a central component of Ethereum's roadmap, enables clients to verify data availability without requiring any single client to download the entire dataset. DAS operates by having clients randomly retrieve individual symbols of erasure-encoded data from a peer-to-peer network. While the cryptographic and encoding aspects of DAS have recently undergone formal analysis, the peer-to-peer networking layer remains underexplored, with a lack of security definitions and efficient, provably secure constructions. In this work, we address this gap by introducing a novel distributed data structure that can serve as the networking layer for DAS, which we call robust distributed arrays. That is, we rigorously define a robustness property of a distributed data structure in an open permissionless network, that mimics a collection of arrays. Then, we give a simple and efficient construction and formally prove its robustness. Notably, every individual node is required to store only small portions of the data, and accessing array positions incurs minimal latency. The robustness of our construction relies solely on the presence of a minimal absolute number of honest nodes in the network. In particular, we avoid any honest majority assumption. Beyond DAS, we anticipate that robust distributed arrays can have wider applications in distributed systems.
The implementation of blockchain technology in tax administration offers promising improvements in security, transparency, and efficiency. This paper presents the design of a blockchain-based e-Faktur system aimed at addressing the challenges of issuing and verifying tax invoices within Indonesia's VAT reporting process. Utilizing Hyperledger Fabric, a private permissioned blockchain, integrated with Hyperledger FireFly, this system ensures data immutability, access control, and decentralized transaction processing. The proposed system streamlines the issuance of NSFP and validates invoices while reducing reliance on centralized servers, eliminating single points of failure, and enhancing auditability. Hypothetical cyberattack scenarios were explored to assess the system's robustness. The results demonstrate that blockchain technology mitigates potential vulnerabilities and improves the resilience of tax reporting systems. The findings highlight the feasibility of blockchain in public tax administration and provide a foundation for future research on large-scale implementations.
Cryptocurrency blockchains, beyond their primary role as distributed payment systems, are increasingly used to store and share arbitrary content, such as text messages and files. Although often non-financial, this hidden content can impact price movements by conveying private information, shaping sentiment, and influencing public opinion. However, current analyses of such data are limited in scope and scalability, primarily relying on manual classification or hand-crafted heuristics. In this work, we address these limitations by employing Natural Language Processing techniques to analyze, detect patterns, and extract public sentiment encoded within blockchain transactional data. Using a variety of Machine Learning techniques, we showcase for the first time the predictive power of blockchain-embedded sentiment in forecasting cryptocurrency price movements on the Bitcoin and Ethereum blockchains. Our findings shed light on a previously underexplored source of freely available, transparent, and immutable data and introduce blockchain sentiment analysis as a novel and robust framework for enhancing financial predictions in cryptocurrency markets. Incidentally, we discover an asymmetry between cryptocurrencies; Bitcoin has an informational advantage over Ethereum in that the sentiment embedded into transactional data is sufficient to predict its price movement.
Kai Ma, Ningyu He, Jintao Huang, B. X. Zhang · 6 authors
Cybersquatting refers to the practice where attackers register a domain name similar to a legitimate one to confuse users for illegal gains. With the growth of the Non-Fungible Token (NFT) ecosystem, there are indications that cybersquatting tactics have evolved from targeting domain names to NFTs. This paper presents the first in-depth measurement study of NFT cybersquatting. By analyzing over 220K NFT collections with over 150M NFT tokens, we have identified 8,019 cybersquatting NFT collections targeting 654 popular NFT projects. Through systematic analysis, we discover and characterize seven distinct squatting tactics employed by scammers. We further conduct a comprehensive measurement study of these cybersquatting NFT collections, examining their metadata, associated digital asset content, and social media status. Our analysis reveals that these NFT cybersquatting activities have resulted in a significant financial impact, with over 670K victims affected by these scams, leading to a total financial exploitation of $59.26 million. Our findings demonstrate the urgency to identify and prevent NFT squatting abuses.
Most blockchains cannot hide the binary code of programs (i.e., smart contracts) running on them. To conceal proprietary business logic and to potentially deter attacks, many smart contracts are closed-source and in many cases exhibit code obfuscation, either intentionally introduced to hide internal logic or unintentionally produced by optimizations. However, we demonstrate that such obfuscation can obscure critical vulnerabilities rather than enhance security, a phenomenon known as insecurity through obscurity. To systematically analyze these risks on a large scale, we present SKANF, a novel EVM bytecode analysis tool tailored for closed-source and obfuscated contracts. SKANF combines control-flow deobfuscation, symbolic execution based on historical transactions to identify and exploit asset management vulnerabilities. Our evaluation on real-world Maximal Extractable Value (MEV) bots reveals that SKANF detects vulnerabilities in 1,030 contracts and successfully generates exploits for 394 of them, with potential losses of \$10.6M. Additionally, we uncover 104 real-world MEV bot attacks that collectively resulted in \$2.76M in losses.
Lingfeng Bao, Jiameng Yang, Xiaohu Yang, Chunming Rong
Since the introduction of Bitcoin in 2008, blockchain technology has garnered widespread attention. Scholars from various research fields, countries, and institutions have published a significant number of papers on this subject. However, there is currently a lack of comprehensive analysis specifically focusing on the scientific publications in the field of blockchain. To conduct a comprehensive analysis, we compiled a corpus of 41,497 publications in blockchain research from 2008 to 2023 using the Clarivate databases. Through bibliometric and citation analyses, we gained valuable insights into the field. Our study offers an overview of the blockchain research landscape, including country, institution, authorship, and subject categories. Additionally, we identified Emerging Research Areas (ERA) using the co-citation clustering approach, examining factors such as recency, growth, and contributions from different countries/regions. Furthermore, we identified influential publications based on citation velocity and analyzed five representative Research Fronts in detail. This analysis provides a fine-grained examination of specific areas within blockchain research. Our findings contribute to understanding evolving trends, emerging applications, and potential directions for future research in the multidisciplinary field of blockchain.
Seung Eel Oh, Jong‐Hoon Kim, Ji-Young Kim, Jae Hwan Ahn
The complexity of contemporary supply chains and the rise in foodborne illness cases have made ensuring food safety and traceability a top responsibility on a worldwide scale. Traditional traceability systems are prone to data tampering, fragmentation, and limited compatibility. Public blockchains have scalability, latency, and privacy problems that limit their use in real-time food safety systems, despite the fact that blockchain provides a secure data structure. Using Hyperledger Fabric, GS1 EPCIS standards, and Internet of Things-enabled environmental sensors, this paper suggests a private blockchain-based food safety monitoring system. To guarantee fault-tolerant, high-throughput processing in a permissioned blockchain setting, a Raft consensus mechanism was used. Hyperledger Caliper was used to benchmark the system once it was deployed with four nodes. According to experimental data, transaction throughput peaked at 230.2 TPS and averaged 207.4 ± 10.2 TPS. As the network grew from two to four nodes, latency increased somewhat from 259.3 ± 9.5 ms to 278.7 ± 9.1 ms, while block finalization time stayed below 3.184 ± 0.113 s. Over 114,925 documented transactions, data integrity was confirmed to be flawless. These results demonstrate that private blockchain technology can provide effective, scalable, and impenetrable food traceability, boosting openness and confidence throughout food networks.
The rapid evolution of blockchain technology and decentralized finance (DeFi) has significantly disrupted traditional financial services globally. DeFi, by leveraging blockchain, enables financial services without relying on traditional intermediaries such as banks, creating a more inclusive, efficient, and transparent financial ecosystem. The chapter explores blockchain and DeFi's impact on the Indian financial services sector. The purpose is to identify how these technologies are transforming financial products, services, and regulations in India while addressing key issues like financial inclusion, security, and scalability. The research methodology involves a qualitative approach, including an analysis of secondary data, case studies of Indian blockchain startups. The study will also provide insights into the regulatory and institutional changes required to support this transformation. In conclusion, while blockchain and DeFi offer significant promise for the Indian financial sector, their adoption requires overcoming technological, regulatory, and cultural barriers.
The rapid growth of cryptocurrency and blockchain technology has raised significant legal and ethical questions, particularly in Muslim-majority countries like Indonesia, where Islamic law (Shariah) plays a central role in financial regulation. This study examines the role of Islamic law in regulating cryptocurrency and blockchain technology, focusing on Indonesia’s regulatory framework. The research aims to assess the compatibility of these technologies with Shariah principles and identify gaps in the current regulatory approach. By doing so, it seeks to provide recommendations for developing a Shariah-compliant regulatory framework that balances innovation with ethical and legal considerations. Using a mixed-methods approach, this study combines legal analysis of Indonesia’s regulatory framework with qualitative interviews with Islamic scholars, regulators, and industry experts. Data were analyzed to evaluate the alignment of cryptocurrency and blockchain technology with Shariah principles, such as the prohibition of riba (interest) and gharar (uncertainty). The findings reveal that while blockchain technology has potential applications in Islamic finance, cryptocurrencies face significant challenges due to concerns over volatility, speculation, and lack of intrinsic value. The study concludes that Indonesia’s regulatory framework must be adapted to address the unique challenges posed by cryptocurrency and blockchain technology while ensuring compliance with Shariah principles.