Marco Ortu, Giacomo Ibba, Giuseppe Destefanis, Claudio Conversano · 5 authors
The expansion of smart contracts on the Ethereum blockchain has created a diverse ecosystem of decentralized applications. This growth, however, poses challenges in classifying and securing these contracts. Existing research often separately addresses either classification or vulnerability detection, without a comprehensive analysis of how contract types are related to security risks. Our study addresses this gap by developing a taxonomy of smart contracts and examining the potential vulnerabilities associated with each category. We use the Latent Dirichlet Allocation (LDA) model to analyze a dataset of over 100,040 Ethereum smart contracts, which is notably larger than those used in previous studies. Our analysis categorizes these contracts into eleven groups, with five primary categories: Notary, Token, Game, Financial, and Blockchain interaction. This categorization sheds light on the various functions and applications of smart contracts in today's blockchain environment. In response to the growing need for better security in smart contract development, we also investigate the link between these categories and common vulnerabilities. Our results identify specific vulnerabilities associated with different contract types, providing valuable insights for developers and auditors. This relationship between contract categories and vulnerabilities is a new contribution to the field, as it has not been thoroughly explored in previous research. Our findings offer a detailed taxonomy of smart contracts and practical recommendations for enhancing security. By understanding how contract categories correlate with vulnerabilities, developers can implement more effective security measures, and auditors can better prioritize their reviews. This study advances both academic knowledge of smart contracts and practical strategies for securing decentralized applications on the Ethereum platform.
Abstract With the rapid expansion of the Internet of Things (IoT), cloud storage has emerged as one of the cornerstones of data management, facilitating ubiquitous access and seamless sharing of information. However, with the involvement of a third party, traditional cloud‐based storage systems are plagued by security and availability concerns, stemming from centralized control and management architectures. A novel blockchain‐IoT model that leverages blockchain technology and decentralized storage mechanisms to address these challenges is presented. The model combines the Ethereum blockchain, interplanetary file system, and attribute‐based encryption to ensure secure and resilient storage and sharing of IoT data. Through an in‐depth exploration of the system architecture and underlying mechanisms, it is demonstrated how the framework decouples storage functionality from resource‐constrained IoT devices, mitigating security risks associated with on‐device storage. In addition, data owners and users can easily exchange data with one another through the use of Ethereum smart contracts, fostering a collaborative environment and providing incentives for data sharing. Moreover, an incentive mechanism powered by the FileCoin cryptocurrency is introduced, which motivates and ensures data sharing transparency and integrity between stakeholders. Furthermore, in the proposed blockchain‐IoT model, the proof‐of‐authority system consensus algorithm has been replaced by a delegated proof‐of‐capacity system, which reduces transaction costs and energy consumption. Using the Rinkby Ethereum official testing network, the proposed model has been demonstrated to be feasible and economical, emphasizing its potential to redefine IoT data management.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
Recently, smart contracts have played a vital role in automatic financial and business transactions. To help end users without programming background to better understand the logic of smart contracts, previous studies have proposed models for automatically translating smart contract source code into their corresponding code summaries. However, in practice, only 13% of smart contracts deployed on the Ethereum blockchain are associated with source code. The practical usage of these existing tools is significantly restricted. Considering that bytecode is always necessary when deploying smart contracts, in this article, we first introduce the task of automatically generating smart contract code summaries from bytecode. We propose a novel approach, named Smart Contract Bytecode Translator ( SmartBT ) for automatically translating smart contract bytecode into fine-grained natural language description directly. Two key challenges are posed for this task: structural code logic hidden in bytecode and the huge semantic gap between bytecode and natural language descriptions. To address the first challenge, we transform bytecode into Control-Flow Graph (CFG) to learn code structural and logic details. Regarding the second challenge, we introduce an information retrieval component to fetch similar comments for filling the semantic gap. Then, the structural input and semantic input are used to build an attentional sequence-to-sequence neural network model. The copy mechanism is employed to copy rare words directly from similar comments, and the coverage mechanism is employed to eliminate repetitive outputs. The automatic evaluation results show that SmartBT outperforms a set of baselines by a large margin, and the human evaluation results show the effectiveness and potential of SmartBT in producing meaningful and accurate comments for smart contract code from bytecode directly.
Ethereum has adopted a rollup-centric roadmap to scale its network while preserving both security and decentralization. Rollups are layer 2 scaling solutions that process transactions off-chain while posting summarized data on-chain to maintain security and reduce costs. Posting data on-chain remains expensive, which led to the introduction of blobs via EIP-4844 that offer a cost-effective solution for data availability (DA). Although blobs significantly reduce DA costs compared to traditional calldata, many cost-sensitive small rollups struggle to fully utilize the fixed blob capacity. Blob sharing, which allows multiple rollups to collaboratively utilize a single blob, has been proposed as a solution to these challenges. In this paper, we empirically analyze nearly six months of data to assess the effectiveness of blob sharing. Our simulation results demonstrate that blob sharing can lower overall costs by approximately $\mathbf{8 0 \%}$ to 99%. These findings imply that the benefits of blob sharing are even greater than initially expected, providing strong incentives for both small and big rollups to actively collaborate in its adoption.
We propose BlockScan, a customized Transformer for anomaly detection in blockchain transactions. Unlike existing methods that rely on rule-based systems or directly apply off-the-shelf large language models (LLMs), BlockScan introduces a series of customized designs to effectively model the unique data structure of blockchain transactions. First, a blockchain transaction is multi-modal, containing blockchain-specific tokens, texts, and numbers. We design a novel modularized tokenizer to handle these multi-modal inputs, balancing the information across different modalities. Second, we design a customized masked language modeling mechanism for pretraining the Transformer architecture, incorporating RoPE embedding and FlashAttention for handling longer sequences. Finally, we design a novel anomaly detection method based on the model outputs. We further provide theoretical analysis for the detection method of our system. Extensive evaluations on Ethereum and Solana transactions demonstrate BlockScan's exceptional capability in anomaly detection while maintaining a low false positive rate. Remarkably, BlockScan is the only method that successfully detects anomalous transactions on Solana with high accuracy, whereas all other approaches achieved very low or zero detection recall scores. This work sets a new benchmark for applying Transformer-based approaches in blockchain data analysis.
Abdul Razzaq, Tao Zhang, Muhammad Numair, Abdulrahman Alreshidi · 9 authors
Abstract Metaverse—a three‐dimensional computational environment—combines physical and virtual reality to enable social relationships and immersive experiences by mimicking real‐world scenarios. Metaverse is considered the third wave of the internet revolution (exploiting Web 3.0), leveraging upcoming technologies such as extended reality and artificial intelligence shaping a new era of human–‐machine interactions. In recent years, increased research and development on educational technologies (EduTech) based on blockchain technology has seen substantial growth of metaverse‐based solutions within the higher education context. This research aims to synergize blockchain technology and metaverse environments to conduct online exams (metaExam) in a trustworthy, reliable, and secure way. The synergy between blockchain and the metaverse brings various benefits, such as improved security, cost effectiveness, and increased efficiency in the online examination process. One of the central features of the proposed solution metaExam is to leverage cryptographic protocols via blockchain to control data access, making verification faster and protecting against misuse. Exam scores and grades are stored on a blockchain ledger using a digital signature method to enhance security. We validated the proposed solution by testing a prototype on the Ethereum platform using the Sepolia Testnet network using Microsoft Windows environment. Evaluation results indicate (i) query response time (10–50 ms), (ii) and query execution performance (CPU utilization between 1%–5%) offering computationally feasible solution. This research contributes by integrating blockchain and metaverse technologies to offer a solution metaExam that can offer improved security and immersive user experience for exam management. The proposed solution and its validation can provide insights into transforming online exams, offering a fresh perspective on addressing concerns about exam grade authenticity and verifying academic credentials in EduTech.
Senay A. Gebreab, Ahmad Musamih, Haya R. Hasan, Khaled Salah · 7 authors
Digital twins and digital artifacts have become integral components of metaverse platforms, providing users with a rich, immersive, and interactive digital experience through the deployment of diverse digital twins and digital artifacts such as 3D avatars, images, and objects. To date, a significant challenge persists in the lack of practical mechanisms to enable seamless teleportation and cross-metaverse interoperability for these digital twins and digital artifacts. There is also a lack of trusted monetization methods that facilitate trading and leasing of digital twins and digital artifacts. To address these important challenges, this paper proposes a blockchain and Non-Fungible Token (NFT)-based solution that facilitates the integration and teleportation of these digital twins and digital artifacts by providing trusted metadata, verifying ownership, and ensuring the authenticity of digital creations in the virtual world. Key to our solution is the introduction of a bridging mechanism that enables cross-metaverse interoperability, allowing for the portable transfer of NFTs across decentralized metaverse platforms. In addition, our solution focuses on empowering original digital creators by enabling the monetization of their creations through the ownership management capabilities offered by NFTs. To reliably and securely store the metadata and content of tokenized digital twins and digital artifacts, we integrate into our solution the Interplanetary File System (IPFS), a decentralized storage system. To demonstrate the feasibility of our solution, we have developed and deployed all necessary smart contracts that govern the main functionalities and interactions of the proposed system on the Ethereum Goerli Testnet. We present our proposed system architecture, accompanied by informative sequence diagrams, algorithms, and testing details. We discuss how our proposed solution attains the main objectives outlined in the paper. We evaluate our proposed solution in terms of cost and security. We have made the complete source code of our smart contracts publicly available on GitHub.
This study presents a blockchain-based traceability system designed specifically for the olive oil supply chain, addressing key challenges in transparency, quality assurance, and fraud prevention. The system integrates Internet of Things (IoT) technology with a decentralized blockchain framework to provide real-time monitoring of critical quality metrics. A practical web application, linked to the Ethereum blockchain, enables stakeholders to track each stage of the supply chain via tamper-proof records. Key functionalities include smart contracts that automate quality checks, ensuring data integrity and providing immediate verification of product authenticity. Initial user feedback highlights the system's potential to enhance transparency and reduce fraud risks in the olive oil market, supporting consumer trust and regulatory compliance. This approach offers a scalable solution adaptable to other high-value agricultural products, demonstrating the blockchain's transformative potential for secure and transparent food traceability.
Blockchain technology has recently received a great deal of attention from industry and academia due to its apparent benefits. From the initial foundation based on cryptocurrency to the development of smart contracts, Blockchain technology continues to promise significant business benefits for various industry sectors. Notwithstanding its known benefits, and despite having some protective measures and security features, this technology still faces significant security challenges within its different abstract layers. This work focuses on the critical cybersecurity threats and vulnerabilities inherent to the different layers of the Blockchain architecture, with a view to mitigate against the associated risks. From the perspective of architectural layering, each layer of the Blockchain has its own corresponding security issues. In this work, a seven-layer architecture is used, whereby the various components of each layer are set out, highlighting the related security risks and corresponding countermeasures. A taxonomy is then developed, that establishes the inter-relationships between the vulnerabilities and attacks in a smart contract. A specific emphasis is placed on the issues caused by centralisation within smart contracts, whereby a “one-owner” controls access, thus threatening the very decentralised nature that Blockchain is based upon. Smart contracts with centralised ownership pose major security issues and act as a single point of failure, allowing single individuals, or teams, to have complete control over the Blockchain network. To mitigate against the risks associated with centralised control, decentralised autonomous organisations (DAOs) promote a decentralised decision-making process whereby the power of decision-making is distributed and therefore preventing smart contract ownership monopoly. The main contribution of this thesis is the development of a novel automated decentralised application, “Genuine DAO”, that promises to reduce security risks and improve the performance of Blockchain networks. “Genuine DAO” achieves the reduction in security risks by enforcing automated rules that are encoded in smart contracts thus reinforcing the community-based governance and minimising the threats inherent to centralisation, which can be caused by smart contracts’ owners/developers. Additionally, “Genuine DAO” strengthens the security of the network by guarding against the threats caused by Frontrunning attacks. Three further contributions emanate from this work. The first one is an improvement of the overall performance of the Blockchain network, through gas optimisation, cost reduction, and network throughput. This is achieved by using a Polygon layer 2 scaling solution built on the Ethereum network. The second one is the development of a general taxonomy that compiles the different vulnerabilities, the types of attacks, and the related countermeasures within each of the seven layers of the Blockchain. The third one stems from a deep dive into one layer of the Blockchain namely, the Contract Layer. A model application is developed depicting, in detail, the security risks within the Contract Layer, while enlisting the best practices and tools to adopt in order to mitigate against these risks. The understanding gained from delving into the details of security risks within the Contract Layer reinforced the need for developing countermeasures to alleviate the security risks and vulnerabilities inherent to one-owner control in smart contracts, which ultimately led to the main contribution of this work: Genuine DAO.
Like any other useful technology, cryptocurrencies are sometimes used for criminal activities. While transactions are recorded on the blockchain, there exists a need for a more rapid and scalable method to detect addresses associated with fraudulent activities. We present RiskSEA, a scalable risk scoring system capable of effectively handling the dynamic nature of large-scale blockchain transaction graphs. The risk scoring system, which we implement for Ethereum, consists of 1. a scalable approach to generating node2vec embedding for entire set of addresses to capture the graph topology 2. transaction-based features to capture the transactional behavioral pattern of an address 3. a classifier model to generate risk score for addresses that combines the node2vec embedding and behavioral features. Efficiently generating node2vec embedding for large scale and dynamically evolving blockchain transaction graphs is challenging, we present two novel approaches for generating node2vec embeddings and effectively scaling it to the entire set of blockchain addresses: 1. node2vec embedding propagation and 2. dynamic node2vec embedding. We present a comprehensive analysis of the proposed approaches. Our experiments show that combining both behavioral and node2vec features boosts the classification performance significantly, and that the dynamic node2vec embeddings perform better than the node2vec propagated embeddings.
Aleksandra Kuzior, Dariusz Krawczyk, Vitaliia Koibichuk, Kseniia Mohylna
This article provides an in-depth analysis of the price dynamics and market prospects of Ethereum, the second-largest cryptocurrency by market capitalization. As blockchain technology and cryptocurrencies increasingly integrate into global financial systems, understanding the factors influencing Ethereum’s price becomes crucial for investors, developers, and researchers. The study uses daily price and volume data from an extensive dataset spanning from 2016 to 2023, focusing on the year 2022 to analyze trends and relationships between Ethereum’s price, market volume, and time. Employing correlation and regression analyses, the study aims to identify key patterns, with a focus on understanding how these variables interact within the volatile cryptocurrency market. The methodology centres on refining the data to ensure accuracy and integrity, including the removal of outliers and verification of variable distributions. Correlation analysis was conducted to explore the relationships between price, volume, and time. Regression analysis further assessed the impact of volume and temporal factors on Ethereum’s price, using heteroskedasticity-consistent standard errors to address market volatility. The model’s robustness was validated through statistical significance tests, and visualizations were used to present data trends and relationships effectively. The findings reveal that Ethereum experienced substantial volatility in 2022, characterized by a general downward price trend. The study identified a weak inverse correlation between price and trading volume, suggesting that periods of higher trading activity often coincide with lower prices, possibly reflecting market corrections or sell-offs. The regression analysis indicated that time is a significant factor in Ethereum’s price dynamics, with a strong positive correlation between the observation order and price, highlighting a clear downward trend over the year. The model demonstrated a high explanatory power, with an Adjusted R-squared of 83.94%, indicating that the selected variables effectively capture the variance in price. The discussion places these findings within the broader context of market developments, including technological shifts like Ethereum 2.0, regulatory changes, and macroeconomic factors that shaped the price movements. The inverse relationship between volume and price underscores the impact of trading behaviour on market sentiment, while the downward temporal trend aligns with the overall market downturn seen in 2022. Despite short-term negative trends, the analysis underscores Ethereum’s long-term potential, given its leading role in decentralized finance, non-fungible tokens, and blockchain innovation. This research remains highly relevant as it addresses the interplay of technical, market, and macroeconomic factors in shaping Ethereum’s price and market prospects, providing a framework for understanding its future trajectory within the evolving cryptocurrency landscape.
This study investigates public sentiment about popular cryptocurrencies listed on crypto exchanges in Turkey, using comments shared on social media platforms and online forums. The research seeks to enhance the existing body of knowledge by overcoming the shortcomings of sentiment analysis studies focused on Turkish texts. Data collected from social media and online forums were examined with sentiment analysis techniques. A total of 607,592 comments were analyzed, of which 89,986 were classified as negative, 72,655 as positive, and 444,951 as neutral. For binary classification, 89,986 negative and 72,655 positive examples were selected and machine-learning models were trained and tested on 162,641 examples. The study's methodology includes an in-depth examination of sentiment analysis results obtained using machine learning classifiers. The findings show how various cryptocurrencies are perceived on different social media platforms. For instance, BTC (Bitcoin) is generally perceived negatively on Investing.com and Telegram, while ETH (Ethereum) generally displays more negative views. These results help investors understand their perceptions and market expectations towards cryptocurrencies. This study deepens the role of social media sentiment analysis in cryptocurrency markets, contributing to the development of new methods and approaches for future research.
Joe Davids, Mohamed El-Sharkawy, Hutan Ashrafian, Eric Herlenius · 5 authors
Abstract Background The use of Cloud-based storage personal health records has increased globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Technical sandboxes allow the capability to simulate different scientific concepts before making them production ready. None exist for the medical fields and cloud-based research. Methods We constructed and tested the sandbox using open-source infrastructures (Ubuntu, Alpine Linux, and Colaboratory) and demonstrated it on a cloud platform. Data preprocessing utilised standard and in-house libraries. The Mina protocol, implementing zero-knowledge proofs, ensured secure blockchain operations, while the Ethereum smart contract protocol within Hyperledger Besu supported enterprise-grade sandbox development. Results Here, we present the GPOC series’ technical sandbox. This is to facilitate future online research and testing of the concept and its security, encryption, movability, research potential, risks and structure. It has several protocols for homomorphic encryption, decentralisation, transfers, and file management. The sandbox is openly available online and tests authorisation, transmission, access control, and integrity live. It invites all committed parties to test and improve the platform. Individual patients, clinics, organisations and regulators are invited to test and develop the concept. The sandbox displays co-ownership of personal health records. Here it is trisected between patients, clinics and clinicians. Patients can actively participate in research and control their health data. The challenges include ensuring that a unified underlying protocol is maintained for cross-border delivery of care based on data management regulations. Conclusions The GPOC concept, as demonstrated by the GPOC Sandbox, represents an advancement in healthcare technology. By promoting patient co-ownership and utilising advanced technologies like blockchain and homomorphic encryption, the GPOC initiative enhances individual control over health data and facilitates collaborative medical research globally. The justification for this research lies in its potential to improve evidence-based medicine and AI dissemination. The significance of the GPOC initiative extends to various aspects of healthcare, patient co-ownership of health data, promoting access to resources and healthcare democratisation. The implications include better global health outcomes through continued development and collaboration, ensuring the successful adoption of the GPOC Sandbox and advancing innovation in digital health.
André Augusto, Rafael Belchior, Jonas Pfannschmidt, André Vasconcelos · 5 authors
Cross-chain bridges are a type of middleware for blockchain interoperability that supports the transfer of assets and data across blockchains. However, several of these bridges have vulnerabilities that have caused 3.2 billion dollars in losses since May 2021. Some studies have revealed the existence of these vulnerabilities, but there is little quantitative research available, and there are no safeguard mechanisms to protect bridges from such attacks. Furthermore, no studies are available on the practices of cross-chain bridges that can cause financial losses. We propose \toolName~(Cross-Chain Watcher), a modular and extensible logic-driven anomaly detector for cross-chain bridges. It operates in three main phases: (1) decoding events and transactions from multiple blockchains, (2) building logic relations from the extracted data, and (3) evaluating these relations against a set of detection rules. Using \toolName, we analyze data from two previously attacked bridges: the Ronin and Nomad bridges. \toolName~was able to successfully identify the transactions that led to losses of \$611M and \$190M (USD) and surpassed the results obtained by a reputable security firm in the latter. We not only uncover successful attacks, but also reveal other anomalies, such as 37 cross-chain transactions (\CCTX) that these bridges should not have accepted, failed attempts to exploit Nomad, over \$7.8M worth of tokens locked on one chain but never released on Ethereum, and \$200K lost by users due to inadequate interaction with bridges. We provide the first open dataset of 81,000 \CCTXS~across three blockchains, capturing more than \$4.2B in token transfers.
Blockchains’ power of decentralization, immutability, and transparency has found its application in many fields. The blockchain is an append-only structure. As the blockchain grows, accessing blocks from the past in an efficient manner has become a challenging task. Further, blockchains were not initially envisioned to be read-heavy systems. With the passage of time, more and more applications are using blockchains and therefore blockchains need to provide active support for high read loads concerning the history state as well. To facilitate data querying in blockchain, we have proposed an SQL query processing feature in the Ethereum blockchain through a decentralized application. More specifically, we build an Ethereum-based Electronic Health Record (EHR) system with SQL query support. The following approaches for query processing have been explored and implemented: (i) linearly scanning blockchain, (ii) scanning only from a user-specified block, (iii) replication in database, (iv) indexing in a database, and (v) using smart contracts. Our timing analysis across these implementations reveals that the smart contracts-based approach has reasonable performance gains compared the other approaches.
Attribute Based Access Control (ABAC) is one the most efficient, scalable, and well used access control. It’s based on attributes not on users, but even when the users want to get access to some resource, they must submit their attributes for the verification process which may reveal the privacy of the users. Many research papers suggest blockchain-based ABAC which provides an immutable and transparent access control system. However, the privacy of the system may be compromised depending on the nature of the attributes. A Zero-Knowledge Proof, Ethereum-Based Access Control (ZK‑ABAC) is proposed in this paper to simplify the management of access to the devices/objects and provide an efficient and immutable platform that keeps track of all actions and access management and preserve the privacy of the attributes. Our ZK-ABAC model utilizes smart contracts to facilitate access control management, Zero-Knowledge Succinct NonInteractive Argument of Knowledge (ZK-SNARK) protocol to add privacy to attributes, InterPlanetary File System (IPFS) network to provide distributed storage system, and Chainlink to manage communications and data between on/ off-chain systems. Comprehensive experiments and tests were conducted to evaluate the performance of our model, including the implementation of ZK-SNARK on the Ethereum blockchain. The results demonstrated the scalability challenges in the setup and proving phases, as well as the efficiency gains in the verification phase, particularly when scaled to higher numbers of users. These findings underscore the practical viability of our ZK-ABAC model for secure and privacy-preserving access control in decentralized environments.
V. Sitharamulu, G. Sucharitha, Sachi Nandan Mohanty, J. Shaik · 5 authors
Modern organ transplant and donor procedures present numerous necessities and also obstacles related to Organ procurement, their delivery, and transplanting are all steps in the enrolment, matchmaking, and disposal process. These processes must adhere to various legal, clinical, ethical, and technical limitations. Consequently, there is a need for a comprehensive organ donation and transplantation system that ensures fairness, efficiency, and an improved experience for patients, ultimately fostering trust in the system. In this research paper, we introduce a ground-breaking solution based on the utilization of a private Ethereum blockchain. This approach revolutionizes the administration of organ gift and transplantation by establishing a copiously reorganized, sheltered, appreciable, auditable, cloistered, as well as truthful framework. Our solution is built upon the development of intelligent smart contracts and is accompanied by the presentation of six sophisticated algorithms, including their thorough enactment, trying, and endorsement procedures. To gauge the effectiveness of our projected result, we undertake an extensive evaluation encompassing privacy, security, and confidentiality analyses. Additionally, we conduct a comprehensive comparison against existing solutions to highlight the advantages and advancements offered by our system. By leveraging the potential of blockchain technology and incorporating smart contracts, our solution addresses the complexities and limitations that have plagued organ donation and transplantation systems. This research aims to enhance the overall efficacy and slide of the procedure, ultimately benefiting disease one in require of life-saving tissue relocates.
While smart farming is becoming increasingly popular as a way to improve the efficiency and sustainability of agroecosystems and global food security, its vulnerability to cyber-attacks is rapidly increasing. A distributed reputation system can help detect and prevent malicious activities by tracking the reputation of IP addresses of devices, products, and entities in a smart farming system, thereby enhancing its security and trust. In this paper, we propose a blockchain-based reputation system for IP addresses in smart farms for real-time intrusion detection and prevention. The study uses the Ethereum blockchain smart contracts, Oracles, Intrusion Detection System (IDS), and a Proof-of-Reputation (PoR) consensus mechanism. The system was evaluated on two datasets, the Spamhaus Blocklist, and the Malware Domain List datasets. It achieved high accuracy, recall, and precision rates, F1 score, as well as low false positive and false negative rates. This shows that the proposed system has high potential to be an effective tool for improving malware detection and prevention in real-time, creating a trusted supply chain for a smart farming ecosystem against a wide range of attack types, including denial-of-service attacks, probe attacks, and user-to-root attacks.
This article presents a Blockchain-based solution for the management of multipolicies in insurance companies, introducing a standardized policy model to facilitate streamlined operations and enhance collaboration between entities. The model ensures uniform policy management, providing scalability and flexibility to adapt to new market demands. The solution leverages Merkle trees for secure data management, with each policy represented by an independent Merkle tree, enabling updates and additions without altering existing policies. The architecture, implemented on a private Ethereum network using Hyperledger Besu and Tessera, ensures secure and transparent transactions, robust dispute resolution, and fraud prevention mechanisms. The validation phase demonstrated the model’s efficiency in reducing data redundancy and ensuring the consistency and integrity of policy information. Additionally, the system’s technical management has been simplified, operational redundancies have been eliminated, and privacy is enhanced.
This study proposes an Automatic Cryptocurrency Trading System using Deep Reinforcement Learning (DRL). Six popular cryptocurrencies were used: Bitcoin, Ethereum, BinanceCoin, DogeCoin, Cardano, and WAVES. Development of the trading system started with building three timeseries models – Temporal Convolutional Neural Network (TCNN), Long Short-Term Memory Network (LSTM), and Gated Recurrent Unit Network (GRU) – to predict future prices. Then, cryptocurrency sentiment data was scraped using the Alternative.me API. Data on historical prices, predicted future prices, cryptocurrency sentiment index, technical indicators, and trading account information was fed as input states to three DRL Agents — Deep Q Network (DQN), Advantage Actor Critic (A2C), and Recurrent Proximal Policy Optimization (RPPO) — which were trained using a custom-developed trading environment. Each agent was given $1000 initial capital for all six cryptocurrencies to trade using three possible actions — Buy, Sell and Hold — and were back-tested on one year of unseen data. Our DQN model had the highest overall return on investment (ROI) of $740, an average 12.3% ROI across all six cryptocurrencies, with an ROI of 63.98% achieved for BinanceCoin. However, A2C and RPPO both had negative ROI.
Inderpreet Singh, Amandeep Kaur, Parul Agarwal, Sheikh Mohammad Idrees
Abstract Most existing e-government services are centralized and rely heavily on human control. This centralized approach makes the system more susceptible to external attacks and compromises data integrity by rogue insiders. Additionally, relying on individuals to monitor and control workflows introduces errors and corruption risks. In order to guarantee security and transparency, this study proposes an automated and decentralized online voting system that makes use of blockchain technology. Compared to conventional voting techniques, it is more efficient and cost-effective, because it eliminates the need of intermediaries. The primary goal of this research is to use blockchain technology to develop a transparent and safe online voting system. In this paper, a decentralized voting system will be developed utilizing ethereum blockchain and smart contracts to ensure the voting process’s integrity. The system can be evaluated with simulated voting data to reflect real-world scenarios, focusing on security, scalability, and user-friendliness. The study also explores potential future enhancements, such as incorporating biometric authentication to further improve accessibility and security. The insights provided will be valuable to policymakers, researchers, and practitioners involved in the development, implementation, and regulation of blockchain-based voting systems.
Open access
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
가상화폐는 일상 속 자산으로 자리 잡고 있으며, 비트코인과 이더리움의 ETF 승인은 제도권에서 받아들이기 시작한 것을 계기로 자산으로서 입지가 강화되고 있다. 우리는 가상화폐를 더 이상 단순 투자의 대상이 아닌 일상에서 화폐로 사용하거나 금과 같이 안전 자산으로 보유하고자 하는 사람들에게 주목한다. 본 연구는 기술지표와 거래 시간을 기반으로 한 가상화폐 보유 전략의 효과성을 분석하며, 이동평균선(MA, Moving Average), 볼린저 밴드(BB, Bollinger Band), 상대강도지수(RSI, Relative Strength Index) 상대강도지수(RSI, Relative Strength Index, 상품 채널 지수(CCI, Commodity Channel Index) 등 기술지표를 활용하여 백테스팅 결과를 수집한다. 우리의 접근 방법을 분석 및 평가하기 위해 매수 후 보유(Buy & Hold) 전략과 비교한다. 비트코인과 이더리움을 대상으로 보유량 증감률, 최대손실률(MDD, Maximum Draw Down), 보유기간 등을 평가한 결과, 기술지표 기반 보유 전략이 가상화폐 보유량 증가에 효과를 보였다. 거래 시간에 따라 보유 전략의 수익률의 차이를 확인하고 적합한 투자 시간을 분석했다. 이는 가상화폐 자산을 보유하면서 보유량의 증가를 원하는 투자자에게 유용한 방법 될 것이라 기대한다.
It is well-known that RANDAO manipulation is possible in Ethereum if an adversary controls the proposers assigned to the last slots in an epoch. We provide a methodology to compute, for any fraction $α$ of stake owned by an adversary, the maximum fraction $f(α)$ of rounds that a strategic adversary can propose. We further implement our methodology and compute $f(\cdot)$ for all $α$. For example, we conclude that an optimal strategic participant with $5\%$ of the stake can propose a $5.048\%$ fraction of rounds, $10\%$ of the stake can propose a $10.19\%$ fraction of rounds, and $20\%$ of the stake can propose a $20.68\%$ fraction of rounds.
Open access
2 source records
Target Tracking and Data Fusion in Sensor Networks
This study empirically investigates the distribution of control and diversity in decentralized autonomous organizations (DAOs) on the Ethereum blockchain. It uses a number of measures-the Nakamoto coefficient, Gini coefficient, Her-findahl-Hirschman index, Theil index, Shannon entropy, and Simpson diversity index-to assess decentralization and fairness in the distribution of control tokens. The analysis reveals a complex landscape in which some DAOs exhibit decentralization with diversity in the distribution of control, while others exhibit significant concentration of power and inequality in the distribution of power. A key observation is the coexistence of a broad distribution of control among EOA addresses with significant inequality in the ownership of control tokens. This indicates that a broad distribution of control does not always correlate with a fair distribution of management tokens. The study also highlights the possibil-ity of delegation mechanisms that may lead to a more equitable distribution of power compared to direct representa-tion. This study provides insight into the governance dynamics of DAOs by demonstrating the complex multi-level bal-ance between decentralization and centralization in a decentralized technology infrastructure.