Bitcoin is the most traded cryptocurrency by volume and market cap. A number of scholars have directed their research towards characterizing Bitcoin’s speculative behavior using a myriad of techniques such as technical analysis, price regression, and direction classification. For this work, research is conducted using the relatively nascent technique of on-chain data analysis. The goal of this research is to evaluate Bitcoin’s on-chain data in predicting future price direction. First, a classification process of on-chain data features that helps the reader understand their relevance is proposed. To address the curse of dimensionality, feature selection algorithms such as L1 regression, Boruta, and the dimensionality reduction algorithm Principal Component Analysis (PCA) are utilized. The research then explores advanced neural networks for next day price direction prediction, including the Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) and the Temporal Convolutional Network (TCN). Neural network models and trading strategies are then compared based on their return statistics. A comparative analysis of feature selection, learning model performance, and trading strategy performance is also conducted. Results from the research show that the Boruta feature selection algorithm combined with the CNN-LSTM model performs best compared to other combinations with a prediction accuracy of 82.03% over the testing period. In addition, the on-chain features within the category, realized value, and unrealized value classifications have higher predictive powers for next day price direction prediction. Finally, during trade simulations, the CNN-LSTM model with a Long-Short strategy had an annualized return of 1682.7% and a Sharpe Ratio of 6.47.
We present a secure and efficient string-matching platform leveraging zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) to address the challenge of detecting sensitive information leakage while preserving data privacy. Our solution enables organizations to verify whether private strings appear on public platforms without disclosing the strings themselves. To achieve computational efficiency, we integrate a sliding window technique with the Rabin-Karp algorithm and Rabin Fingerprint, enabling hash-based rolling comparisons to detect string matches. This approach significantly reduces time complexity compared to traditional character-by-character comparisons. We implement the proposed system using gnark, a high-performance zk-SNARK library, which generates succinct and verifiable proofs for privacy-preserving string matching. Experimental results demonstrate that our solution achieves strong privacy guarantees while maintaining computational efficiency and scalability. This work highlights the practical applications of zero-knowledge proofs in secure data verification and contributes a scalable method for privacy-preserving string matching.
In this article, we propose a new form of decentralized autonomous organization (DAO) governance that uses a sequential auction mechanism to overcome the entrenched control issues that have emerged for DAOs by creating a regime of temporary contestable control. The mechanism avoids potential public choice problems inherent in voting approaches but at the same time provides a vehicle that can enhance and secure value that inheres to DAO voting and other DAO non-market governance procedures. It is robust to empty voting and is code feasible. The mechanism not only facilitates the ability of DAOs to meet their normative and operational goals in the face of diverse regulatory approaches, but also strengthens the case for creating a less burdensome but at least equally effective regulatory regime for DAOs that employ the mechanism. Designed to shift control to the party with the most promising business plan, at the same time, it deters value destruction by control parties, maximizes social surplus, and distributes that surplus in a way that tends to promote investment by other parties both at start up and on an ongoing basis.
Tomaž Berlec, Marko Corn, Sergej Varljen, Primož Podržaj
The Fourth Industrial Revolution has introduced “shared manufacturing” as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems.
Federated Learning (FL) enables collaborative model training across hospitals while keeping patient data local, thus aiming to satisfy strict healthcare privacy regulations (e.g. HIPAA, GDPR). However, FL still leaks information via shared model updates, exposing it to membership inference and gradient inversion attacks. In this work, we propose an end-to-end framework that integrates zero-knowledge proofs (ZKPs) with FL to ensure both data privacy and trust in the aggregation process. In our design, each hospital (client) sends encrypted model updates to a central aggregator, which then computes the global model and simultaneously generates a succinct ZKP (e.g. a zk-SNARK) attesting to the correctness of the aggregation. Clients (or a verifier network) can efficiently verify this proof without learning any additional information. We simulate a disease-prediction task on synthetic medical data and evaluate metrics including predictive accuracy, proof generation/verification time, and communication overhead. Our results (see Table 1 and Fig. 3) show that incorporating ZKP maintains almost identical model accuracy compared to standard FL while adding moderate computational and bandwidth overhead. ZKP verification costs scale favorably (often <50% of proof generation time) and can be offloaded to a blockchain network to avoid burdening resource-constrained hospitals. The key contribution is a structured ZK-FL framework combining FL and zk-SNARKs, along with a formal threat model. This approach closes FL’s trust gap in healthcare settings, and suggests future work on scalable proof systems (e.g. post-quantum ZKPs) and integration with blockchain-based verifiers.
Pietro Saggese, Michael Fröwis, Stefan Kitzler, Bernhard Haslhofer · 5 authors
Total Value Locked (TVL) aims to measure the aggregate value of cryptoassets deposited in Decentralized Finance (DeFi) protocols. Although blockchain data is public, the way TVL is computed is not well understood. In practice, its calculation on major TVL aggregators relies on self-reports from community members and lacks standardization, making it difficult to verify published figures independently. We thus conduct a systematic study on 939 DeFi projects deployed in Ethereum. We study the methodologies used to compute TVL, examine factors hindering verifiability, and ultimately propose standardization attempts in the field. We find that 10.5% of the protocols rely on external servers; 68 methods alternative to standard balance queries exist, although their use decreased over time; and 240 equal balance queries are repeated on multiple protocols. These findings indicate limits to verifiability and transparency. We thus introduce ``verifiable Total Value Locked'' (vTVL), a metric measuring the TVL that can be verified relying solely on on-chain data and standard balance queries. A case study on 400 protocols shows that our estimations align with published figures for 46.5% of protocols. Informed by these findings, we discuss design guidelines that could facilitate a more verifiable, standardized, and explainable TVL computation.
Aikaterini-Panagiota Stouka, Julian Ma, Thomas Thiery
Transaction Fee Mechanism (TFM) design in blockchain protocols has gained significant attention following the pioneering work of Roughgarden [EC' 21], which established a formal framework for analyzing user and block proposer incentives under various Transaction Fee Mechanisms, including Ethereum's current fee mechanism EIP-1559. However, the original TFM framework and follow-up TFM works overlook the critical challenge of censorship resistance-specifically in the presence of an external malicious actor who is willing to bribe the proposer to censor a transaction. In this paper, we extend the Roughgarden's framework to capture censorship resistance under bribery attacks via a Bayesian game, where a strategic block proposer's "type" is determined by a bribe function from an external malicious actor. Under this framework, the definition of a standard TFM is extended to a bribery-aware TFM. This technique is broadly applicable to analyze censorship resistance under bribery attacks of both single and multiple proposer protocols within the original TFM scope. We choose to utilize it to evaluate the incentive compatibility and censorship resistance of several TFMs within the context of a multiple proposer protocol called Fork-Choice Enforced Inclusion Lists (FOCIL). FOCIL represents a critical evolution in the Ethereum roadmap, serving as the consensus and censorship resistance flagship for the upcoming Hegota hard fork. It aims to bolster Ethereum's censorship resistance by enabling multiple proposers to contribute to block construction. While recent works such as Garimidi et al.[FC' 25] have extended the TFM framework to multiple proposer settings, they do not aim to capture censorship under bribery attacks and they are not compatible with the unique hierarchical structure of FOCIL.
Smart railways integrate advanced information technologies into railway operating systems to improve efficiency and reliability. Although the development of 5G has enhanced railway services, future smart railways require ultra-high speeds, ultra-low latency, ultra-high security, full coverage, and ultra-high positioning accuracy, which 5G cannot fully meet. Therefore, 6G is envisioned to provide green and efficient all-day operations, strong information security, fully automatic driving, and low-cost intelligent maintenance. To achieve these requirements, we propose an integrated network architecture leveraging communications, computing, edge intelligence, and caching in railway systems. We have conducted in-depth investigations on key enabling technologies for reliable transmissions and wireless coverage. For high-speed mobile scenarios, we propose an AI-enabled cross-domain channel modeling and orthogonal time-frequency space-time spread multiple access mechanism to alleviate the conflict between limited spectrum availability and massive user access. The roles of blockchain, edge intelligence, and privacy technologies in endogenously secure rail communications are also evaluated. We further explore the application of emerging paradigms such as integrated sensing and communications, AI-assisted Internet of Things, semantic communications, and digital twin networks for railway maintenance, monitoring, prediction, and accident warning. Finally, possible future research and development directions are discussed.
Causal networks offer an intuitive framework to understand influence structures within time series systems. However, the presence of cycles can obscure dynamic relationships and hinder hierarchical analysis. These networks are typically identified through multivariate predictive modelling, but enforcing acyclic constraints significantly increases computational and analytical complexity. Despite recent advances, there remains a lack of simple, flexible approaches that are easily tailorable to specific problem instances. We propose an evolutionary approach to fitting acyclic vector autoregressive processes and introduces a novel hierarchical representation that directly models structural elements within a time series system. On simulated datasets, our model retains most of the predictive accuracy of unconstrained models and outperforms permutation-based alternatives. When applied to a dataset of 100 cryptocurrency return series, our method generates acyclic causal networks capturing key structural properties of the unconstrained model. The acyclic networks are approximately sub-graphs of the unconstrained networks, and most of the removed links originate from low-influence nodes. Given the high levels of feature preservation, we conclude that this cryptocurrency price system functions largely hierarchically. Our findings demonstrate a flexible, intuitive approach for identifying hierarchical causal networks in time series systems, with broad applications to fields like econometrics and social network analysis.
Musharraf Alruwaill, Saraju Mohanty, Elias Kougianos
Concerns regarding privacy and data security in conventional healthcare prompted alternative technologies. In smart healthcare, blockchain technology addresses existing concerns with security, privacy, and electronic healthcare transmission. Integration of Blockchain Technology with the Internet of Medical Things (IoMT) allows real-time monitoring of protected healthcare data. Utilizing edge devices with IoMT devices is very advantageous for addressing security, computing, and storage challenges. Encryption using symmetric and asymmetric keys is used to conceal sensitive information from unauthorized parties. SHA256 is an algorithm for one-way hashing. It is used to verify that the data has not been altered, since if it had, the hash value would have changed. This article offers a blockchain-based smart healthcare system using IoMT devices for continuous patient monitoring. In addition, it employs edge resources in addition to IoMT devices to have extra computing power and storage to hash and encrypt incoming data before sending it to the blockchain. Symmetric key is utilized to keep the data private even in the blockchain, allowing the patient to safely communicate the data through smart contracts while preventing unauthorized physicians from seeing the data. Through the use of a verification node and blockchain, an asymmetric key is used for the signing and validation of patient data in the healthcare provider system. In addition to other security measures, location-based authentication is recommended to guarantee that data originates from the patient area. Through the edge device, SHA256 is utilized to secure the data's integrity and a secret key is used to maintain its secrecy. The hChain architecture improves the computing power of IoMT environments, the security of EHR sharing through smart contracts, and the privacy and authentication procedures.
The main motivation of this thesis is the uncertain panorama of cybersecurity risks and threats, accentuated by the arrival of the quantum computer. This type of computer is completely disruptive, since its operation is governed by quantum mechanical phenomena. The implementation of Shors algorithm in a quantum computer with relevant size and performance will allow breaking the security of the most currently used pre-quantum asymmetric algorithms. This panorama makes it necessary to research new cryptographic paradigms that are resistant to quantum threats. Thus, quantum and post-quantum cryptography emerge. Several national security agencies are recommending the immediate migration to quantum-resistant solutions of vulnerable critical cryptosystems, mainly by implementing post-quantum algorithms, some of them recently standardized. Quantum cryptography bases its security on the same physical foundations as quantum computers, being independent of the computational capacity of an adversary. The implementation of solutions based on quantum cryptography still requires greater technological maturity, development of standards and certification of devices. In addition, the infrastructures necessary for these networks are expensive and difficult to scale, in their current conception, due to the need to have trusted intermediate nodes. However, the rapid advances in this field allow to further research quantum communications networks to be a reality for daily operations where a high level of security is required. The main objective of this thesis is to investigate quantum cryptography-based solutions that go beyond quantum key distribution (QKD). The thesis has focused on proposing two novel cryptographic mechanisms ensuring that the new protocols are comparable in efficiency with pre-quantum and post-quantum algorithms. Furthermore, it has been taken into account that these protocols are implementable in current quantum communications infrastructures (QCI) to maximize the technical benefit of the investments carried out for these deployments. As a result, a quantum-assisted digital signature protocol (Q-DS) and a quantum zero-knowledge proof (QZKP) have been proposed, analyzed and implemented, which combine symmetric pre-quantum mechanisms with QKD. The proposed quantum-assisted digital signature protocol avoids the use of vulnerable pre- quantum public-key cryptosystems, using symmetric keys generated by QKD and using them with widely known NIST-approved hash functions, giving rise to a composite cryptosystem whose security against various attacks is demonstrated. For its part, the proposed quantum zero-knowledge proof allows the authentication of users in a QCI without revealing personal information during the process. The proposal of a quantum version of ZKP has been done in this thesis for the very first time, without precedent in the literature. A theoretical study as well as experimental tests have been carried out, resulting in a secure and efficient authentication mechanism. Finally, given the industrial nature of this thesis, the evolution of the political panorama regarding quantum technologies and PQC have been closely followed, including the positions of relevant security-oriented organizations and economic investments for project funding. These issues, although not technical, have influenced the design of the cryptographic protocols proposed in this thesis. RESUMEN La principal motivación de esta tesis es el panorama incierto de los riesgos y amenazas de ciberseguridad, acentuado por la llegada del ordenador cuántico. Este tipo de ordenadores son completamente disruptivos, ya que su funcionamiento se rige por fenómenos mecánico-cuánticos. La implementación del algoritmo de Shor en un ordenador cuántico con tamaño y rendimiento relevantes permitirá romper la seguridad de los algoritmos asimétricos pre-cuánticos más utilizados actualmente. Este panorama hace necesario investigar nuevos paradigmas criptográficos que sean resistentes a las amenazas cuánticas. Así, surgen la criptografía cuántica y post-cuántica. Varias agencias de seguridad nacional han recomendado la migración inmediata de los criptosistemas críticos vulnerables a soluciones "quantum-resistant", principalmente mediante la implementación de algoritmos post-cuánticos, algunos de ellos recientemente estandarizados. La criptografía cuántica basa su seguridad en los mismos fundamentos físicos que los ordenadores cuánticos, siendo independiente de la capacidad computacional de un adversario. La implementación de soluciones basadas en criptografía cuántica aún requiere de mayor madurez tecnológica, desarrollo de estándares y certificación de dispositivos. Además, las infraestructuras necesarias para estas redes son costosas y difíciles de escalar, en su concepción actual, debido a la necesidad de contar con nodos intermedios de confianza. Sin embargo, los rápidos avances en este campo permiten que la investigación de las redes de comunicaciones cuánticas se vaya convirtiendo en una realidad para las operaciones diarias donde se requiere un alto nivel de seguridad. El objetivo principal de esta tesis es investigar soluciones basadas en criptografía cuántica que vayan más allá de la distribución de claves cuánticas (QKD). La tesis se ha centrado en proponer dos mecanismos criptográficos novedosos asegurando que los nuevos protocolos sean comparables en eficiencia con algoritmos pre-cuánticos y post-cuánticos. Además, se ha tenido en cuenta que estos protocolos sean implementables en las actuales infraestructuras de comunicaciones cuánticas (QCI) para maximizar el beneficio técnico de las inversiones realizadas para estos despliegues. Como resultado, se han propuesto, analizado e implementado un protocolo de firma digital asistido por claves cuánticas (Q-DS) y una prueba de conocimiento cero cuántica (QZKP), que combinan mecanismos pre-cuánticos simétricos con QKD. El protocolo de firma digital cuántica propuesto evita el uso de criptosistemas de clave pública pre-cuánticos vulnerables, utilizando claves simétricas generadas por QKD y utilizándolas con funciones hash ampliamente conocidas aprobadas por el NIST, dando lugar a un criptosistema compuesto cuya seguridad frente a diversos ataques se demuestra. Por su parte, la QZKP propuesta permite la autenticación de usuarios en una QCI sin revelar información personal durante el proceso. La propuesta de una versión cuántica de ZKP se ha realizado en esta tesis por primera vez, sin precedentes en la literatura. Se ha realizado un estudio teórico así como pruebas experimentales, dando como resultado un mecanismo de autenticación seguro y eficiente. Finalmente, dada la naturaleza industrial de esta tesis, se ha seguido de cerca la evolución del panorama político en relación con las tecnologías cuánticas y PQC, incluyendo las posiciones de las organizaciones relevantes en materia de seguridad y las inversiones económicas para la financiación de proyectos. Estas cuestiones, aunque no técnicas, han influido en el diseño de los protocolos criptográficos propuestos en esta tesis.
Inzamam Shahzad, Muhammad Wajid Maqsood, Sadia Latif, Hafiz Muhammad Ijaz
As communication technologies evolve, the IoT has transitioned from nascent development to near maturity, driving exponential growth in data transmission and processing. This advancement imposes increasingly stringent performance requirements on the management of globally distributed IoT infrastructures. Current centralized IoT device management platforms, however, face critical technical limitations, including vulnerability to cyber-attacks, single points of failure, and scalability challenges. To address these issues while adhering to regulatory mandates for data confidentiality, this study proposes a blockchain-integrated IoT sensor system designed to enhance data security, transparency, and accessibility. The framework combines IoT-based sensor networks with blockchain technology to establish an immutable, decentralized ledger for device interactions, ensuring tamper-resistant data records and secure access control. A smart contract governs the application’s business logic, automating rules for user-device interactions, data monitoring, and device management. The system’s efficacy is validated through a prototype implementation using NodeMCU microcontrollers and permissioned blockchain networks, with performance evaluated across metrics such as latency, throughput, and resource utilization. A case study in cotton field agriculture demonstrates the platform’s practical application, integrating irrigation automation to optimize water consumption. Empirical results indicate a 35% reduction in water usage while maintaining crop yield, alongside robust resistance to unauthorized data tampering. Comparative analysis highlights the solution’s superiority over centralized alternatives in scalability and resilience, particularly for resource-constrained IoT environments. By harmonizing IoT’s sensing capabilities with blockchain’s decentralized security, this work advances agricultural management practices, offering a robust, transparent, and efficient paradigm for modern IoT deployments. The findings underscore the transformative potential of blockchain-IoT integration in fostering sustainable, data-driven decision-making across diverse industrial sectors.
A democratic election is a crucial act in each nation, as it determines the country's future for a specific term. Some of the older voting methods, such as Ballot Paper and EVM (Electronic Voting Machine), have disadvantages such as lack of transparency, poor voter turnout, vote rigging, and many others. Using Blockchain technology and Smart Contracts, it is simple to circumvent the flaws of the Ballot system and EVM. Electronic Voting Powered by Blockchain and Smart Contracts outperforms these antiquated voting methods by delivering secure results in less time and at a lower cost. With E-Voting utilizing Blockchain, prices can be lowered, the necessity for Polling stations and the consumption of resources such as EVMs and Ballot Papers may be decreased, and security can be improved by offering End-to-End Encryption and authenticity. This blockchain-powered e-voting can readily acquire trust due to the transaction's transparency, immutability, and difficulty of modification once hosted, as a result of smart contracts. Using OTP Verification and face verification, the suggested solution is a MERN-based web application with a multitude of upgraded authentication and permission techniques. To improve security, this voting data is saved as a transaction in a Blockchain-based distributed ledger using smart contracts.
Kevin Darmawan, Lastuti Abubakar, Tri Handayani, Dewi Kania Sugiharti
The development of financial technology has driven the emergence of digital assets such as Bitcoin, which challenge the conventional legal framework of property law. Although not recognized as legal tender, Bitcoin has been acknowledged as a legal asset by Indonesia's Commodity Futures Trading Regulatory Agency (Bappebti). This study aims to examine the legal status of Bitcoin as collateral in Indonesia’s property law framework, as well as assess the adequacy of current regulations in accommodating this function. This research employs a normative juridical method with a qualitative approach and deductive reasoning. The findings indicate that Bitcoin fulfills the legal criteria of an intangible object and, in theory, can be used as an object of fiduciary security. However, the absence of explicit legal recognition and a digitally integrated collateral registration system has led to legal uncertainty. Although regulations in Indonesia have provided a degree of legality and oversight, systemic institutional integration remains lacking. Therefore, legal reform is needed in the area of digital asset-based collateral, along with the establishment of a comprehensive administrative registration system to ensure the validity of security interests in digital assets.
The developing Sixth-Generation (6G) network aims to establish seamless global connectivity for billions of humans, machines, and devices. However, the rich digital service and explosive heterogeneous connection between various entities in 6G networks can not only induce increasing complications of digital identity management but also raise material concerns about the security and privacy of user identity. In this paper, we design a user-centric identity management that returns the sole control to the user self and achieves identity sovereignty towards 6G networks. Specifically, we propose a blockchain-based Identity Management (IDM) architecture for 6G networks, which provides a practical method to secure digital identity management. Subsequently, we develop a fully privacy-preserving identity attribute management scheme by using zero-knowledge proof to protect the privacy-sensitive identity attribute. In particular, the scheme achieves an identity attribute hiding and verification protocol to support users in obtaining and applying their identity attributes without revealing concrete data. Finally, we analyze the security of the proposed architecture and implement a prototype system to evaluate its performance. The result shows that our proposed architecture can ensure that users effectively manage their digital identity in 6G networks.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
This paper examines the dynamics of trust and bot-driven responses within the meme coin ecosystem on the Solana blockchain, with a particular emphasis on the interplay between social media-induced sentiment and on-chain transaction behaviors.Meme coins, which originate from internet culture and are heavily influenced by community sentiment, represent a volatile and distinct category of cryptocurrencies.Employing sentiment propagation networks, on-chain transaction data, and sentiment-transaction integrated models, we quantitatively analyze the relationship between emotional fluctuations and market behaviors.By contrasting Rug Pull scams with sustainable projects, we identify critical differences in the role of sentiment across different phases of project development.Our findings reveal three distinct sentiment-driven user trading behaviors: sentiment followers, makers, and stabilizers.The results indicate that, while sentiment is a primary driver of early-stage trading within Rug Pull projects, its influence diminishes as community distrust intensifies, resulting in more opportunistic and reactive trading patterns.This study contributes to the understanding of the co-evolution of memes, sentiment, and market dynamics, offering new insights into the complexities of decentralized finance ecosystems, with a specific focus on Solana-based meme coin markets.
Felipe Mello Fonseca, M.F.S.F. de Moura, Pedro Henrique González, Diogo Mendonça
A Blockchain é uma tecnologia inovadora aplicada em diversas áreas como finanças, gestão de registros, votação eletrônica e jogos. As transações em blockchain são frequentemente executadas por smart contracts, código considerado crítico em termos de segurança. Existem diversas ferramentas que se propõe a identificar vulnerabilidades de forma automatizada em smart contracts, contudo, conforme estudos anteriores mostram, a eficácia nesta tarefa é normalmente baixa. Desse modo, identificar vulnerabilidades de forma automatizada em smart contracts continua sendo um grande desafio. Neste estudo investigamos a evolução de duas importantes ferramentas para detecção de vulnerabilidades em smart contracts: Mythril e Slither, avaliando sua eficácia na identificação de vulnerabilidades e se evoluíram comparadas a uma versão anterior. Para isto, executamos as ferramentas com versões mais rescentes em um conjunto de 69 smart contracts previamente analisados em um estudo anterior. Os resultados foram comparados com as vulnerabilidades já classificadas e submetidos a uma validação manual para aferir sua precisão. Os experimentos demonstram que Mythril apresentou melhorias na redução de falsos positivos, enquanto Slither aprimorou a detecção de falhas relacionadas a Access Control. Contudo, as ferramentas apresentaram limitações na sua evolução para alguns tipos de vulnerabilidades. Esses achados reforçam a necessidade contínua de aprimoramento e avaliação das ferramentas de análise automatizada de segurança em smart contracts.
Blockchain platforms used for the exchange of decentralized digital assets regularly attract swindlers, hackers, and scammers. For ‘honest’ traders, such individuals and organizations betray the Anarcho-Libertarian spirit of collaborative experimentation that accompanied the crypto ‘ecosystem’ in the previous decade. Members of the many crypto communities that interact online therefore invest efforts in identifying and then excluding dishonest actors, mainly by digitally tracking accounts and users. Based on fieldwork with non-fungible tokens (NFTs) collectors in London, I will focus on the temporality of such acts of monitoring, which I argue constitute a post-panoptic reality wherein masking becomes intrinsic to interactions online or offline. The analysis rethinks the connection between play, uncertainty, and surveillance in conditions of hypervisibility.
Hiago Vinícius Benedito dos Santos, Raissa Rosa dos Santos Januario, Ravelly Carvalho Zanatta, Saulo Neves Matos · 5 authors
In recent years, blockchain technology has established itself as an effective, secure, and transparent data storage solution. In this context, smart contracts play a fundamental role by enabling the automated execution of agreements without intermediaries. With the advancement of language models, the opportunity to automatically generate these contracts has emerged, raising concerns about their reliability and potential vulnerabilities. This article proposes a comparative analysis of the available language models for developing smart contracts using Ethereum Virtual Machine’s contracts as a case study. Experiments were made using various Large language models using different metrics to evaluate the susceptibility to vulnerabilities and computational cost. After comparing various models, ChatGPT appears to be the most suitable for generating smart contracts due to its higher compilation rate and, consequently, a larger sample size, despite detecting more vulnerabilities.
Mohammad Seyfi, Mehdi Mehdinejad, Behnam Mohammadi-Ivatloo, Jamshid Aghaei
The increasing integration of distributed renewable energy resources has transformed consumers into prosumers. Peer-to-peer (P2P) energy trading markets have emerged as a promising solution to facilitate prosumer participation in energy markets. This paper proposes a novel market model for local P2P energy token trading, integrating self-decisive retailers and smallscale prosumers with flexible loads. Within the proposed market structure, retailers as self-decisive agents, can participate in a dynamic and competitive market, ensured by demurrage mechanism, and connect local prosumers to upper energy markets. In this model, local prosumers can choose either directly trade energy tokens with retailers or with other prosumers in the P2P market. Moreover, by having both shiftable and cuttable loads, prosumers can manage their energy costs more effectively and improve their trading strategy. The simulation results showed the importance of the demand response program in promoting the independence of the local P2P energy token trading market. Furthermore, results show retailers can increase the welfare of local prosumers in a local P2P energy token market.
Pedro Henrique Barcha Correia, Marcos A. Simplício
Despite its environmental benefits, forestation is often overlooked in favor of more profitable crops with negative environmental impacts. In response, this work promotes forestation through tokenization, creating financial incentives for landowners who plant trees. Forested areas and green assets are transformed into Non-Fungible Tokens (NFTs) on a consortium blockchain. Their metadata is automatically updated via satellite-based deforestation tracking systems, something unseen in the literature. The solution also contributes to distributed systems and the environment by proposing several transparency mechanisms – often lacking in forestation efforts and permissioned blockchains –, enabling authorities to audit the transaction and utilization of green assets.
Carlos Melo, José Miqueias, Glauber Dias Gonçalves, Francisco Airton Silva · 6 authors
Embora a transição da plataforma Ethereum para Proof-of-Stake e o surgimento de sidechains ofereçam soluções parciais para os problemas de escalabilidade, essas abordagens apresentam trade-offs entre segurança e complexidade de implementação. Para mitigar esses desafios, os ZK-Rollups surgiram como soluções de escalabilidade de Layer-2, combinando computação off-chain com verificação on-chain, garantindo segurança e descentralização na plataforma Ethereum. Este artigo propõe uma abordagem baseada em Redes de Petri Estocásticas para avaliar a viabilidade dos ZK-Rollups, considerando os principais fatores que impactam métricas de desempenho essenciais, como vazão e latência. Também analisamos a relação entre custo e benefício, incluindo o custo médio por transação e como este é impactado pelas métricas de desempenho. Os resultados mostram que uma maior adoção de transações na Layer-2 pode aumentar a vazão do sistema em até 20%, passando de 85 tps em um ambiente sem Layer-2 para 105 tps quando 90% das transações seguem por esse caminho. Por outro lado, a latência pode sofrer um aumento superior a 100% com a utilização de batches maiores na Layer-2.
The rise of social media has significantly influenced the cryptocurrency market, driving volatility through sentiment-driven trading. This study employs a bibliometric and content analysis approach to examine how social media, particularly Twitter, impacts cryptocurrency price movements. Using the bibliometric analysis, 151 peer-reviewed articles published between 2018 and 2024 were analyzed to identify key research trends, themes, and potential future research. This study finds that social media sentiment plays a crucial role in cryptocurrency price forecasting, with machine learning and natural language processing (NLP) techniques enhancing prediction accuracy. Thematic analysis reveals four primary areas of focus: sentiment analysis and market prediction, machine learning-driven algorithmic trading, blockchain investment risks, and influencer-driven market behavior. This study contributes to the field by consolidating existing social media sentiment and cryptocurrency valuation knowledge, offering insights to investors, regulators, and academics. It highlights the need for future research to integrate multi-platform sentiment analysis, regulatory considerations, and behavioral finance perspectives. These insights are vital for understanding the evolving landscape of digital asset markets and their susceptibility to sentiment-driven speculation.
Fábio R. Silva, Bruno B. Neves, Henrique Fan, Roben Castagna Lunardi · 6 authors
A rastreabilidade na cadeia produtiva de vegetais frescos tem ganhado relevância, especialmente no Brasil, onde a Instrução Normativa Conjunta (INC) 02/2018 estabelece a obrigatoriedade de rastreamento desses alimentos, influenciando também políticas como a destinação de 30% dos recursos da alimentação escolar para a agricultura familiar. Além disso, pesquisas de mercado indicam uma crescente preferência dos consumidores por produtos locais e sustentáveis. Neste contexto, este trabalho propõe uma solução baseada em smart contracts para rastrear a cadeia produtiva de vegetais frescos provenientes da agricultura familiar. O estudo inclui uma análise da INC 02/2018 e apresenta uma solução tecnológica, avaliando sua viabilidade econômica em diferentes redes de blockchains (públicas e privadas) para atender populações de múltiplas regiões geográficas.