Introduction There is a continuum between gambling and investing behaviors, with speculative investment instruments positioned in the middle. Cryptocurrencies, being significantly more volatile than traditional investment tools, have increasingly been linked to gambling disorder (GD). This study aims to examine the relationship between cryptocurrency trading behavior and GD, high-risk substance use, high-risk alcohol use, and tobacco dependence among healthcare professionals in Türkiye. Methods A total of 192 healthcare professionals were assessed using the Problematic Cryptocurrency Trading Scale (PCTS), Gambling Disorder Screening Test (GDST), and the Addiction Profile Index Risk Screening Form (APIRS) (Alcohol and Drug Scales). Categorical data comparisons between two independent groups were conducted using Chi-square or Fisher’s Exact tests. Spearman correlation coefficients were used to examine relationships between PCTS scores and APIRS/GDST scores. Additionally, linear regression models assessed the predictive relationships between PCTS scores and APIRS/GDST scores. Results Among the participants, 25.5% reported engaging in cryptocurrency trading, 41.7% had tobacco dependence, 15.1% reported high-risk alcohol use, 5.7% had high-risk substance use, and 8.9% met the criteria for GD. Cryptocurrency traders demonstrated higher rates of substance use ( p = 0.033), tobacco dependence ( p < 0.001), and GD ( p = 0.043). Additionally, the severity of problematic cryptocurrency trading behavior was positively correlated with the severity of substance use ( r = 0.172, p = 0.017) and GD ( r = 0.455, p < 0.001). Conclusion The findings indicate a significant relationship between cryptocurrency trading behavior and addiction. Further research with clinical interviews and larger sample sizes is required to validate these findings. The high rates of alcohol, substance, tobacco, and gambling addictions observed among healthcare professionals underscore the need for targeted preventive measures and interventions in this population.
Aviation plays a crucial role in global transportation, providing an essential service for passengers and cargo and contributing significantly to economic growth and international connectivity. However, the increasing number of aircraft accidents and high repair costs emphasize the need for robust maintenance systems. With mechanical failures accounting for 36% of aviation accidents, a secure and efficient preventive maintenance system is essential to enhance safety and reduce downtime. Traditional methods suffer from delayed inspections, fragmented documentation, and inefficiencies in spare parts management, leading to increased operational costs and downtime. This paper proposes a blockchain-based solution integrating Ethereum smart contracts, Time Oracles, and InterPlanetary File System (IPFS) to automate maintenance scheduling, ensure data integrity, and enhance traceability. Time Oracles trigger maintenance at predefined intervals, ensuring timely inspections, while IPFS securely stores maintenance records, preventing data loss or tampering. Smart contracts automate maintenance workflows and spare parts procurement, improving efficiency and accountability. This paper details the system architecture, underlying algorithms, and rigorous testing and validation processes. Furthermore, a cost and security analysis evaluates the solution’s effectiveness, demonstrating its potential to significantly enhance safety, reduce operational costs, and improve transparency across aviation maintenance protocols.
This study examines the role of crypto funds (CFs) in enhancing the valuation and performance of decentralized digital platforms (DDPs) by mitigating coordination frictions and information asymmetries. Drawing on panel data from 1,200 Ethereum-based projects and event-study evidence around CF investment disclosures, we find that CF-backed DDPs achieve significantly higher token valuations in the primary market, experience positive cumulative abnormal returns (CARs) around investment announcements, and outperform non-CF-backed peers’ post-issuance. The impact of CFs is stronger when they hold central positions in investor networks and when token ownership is more decentralized. Robustness checks using alternative dependent variables, subsample analyses, and interaction terms confirm the validity of the findings. These results highlight the importance of institutional capital not only in financing but also in signaling quality and enhancing governance in decentralized ecosystems. Policy implications include the need for standard CF disclosure practices, token distribution guidelines, and improved audit standards for smart contracts. The findings contribute to emerging debates on institutional legitimacy, valuation dynamics, and governance in the digital asset economy.
Abstract While Ethereum smart contracts provide users with transfer and transaction services, vulnerabilities in smart contracts are constantly damaging users’ property and user experience. At present, many detection methods for smart contract vulnerabilities have been proposed, but these methods have not fully analyzed the information of multiple modalities of smart contracts, and their effectiveness in detecting long smart contracts is not ideal. We propose a lightweight Ethereum smart contract vulnerability detection method based on bimodal and hierarchical attention to address this issue. This method can combine the source code and opcode of smart contracts for analysis, and use a hierarchical attention network composed of bidirectional GRU and attention mechanism for vulnerability feature extraction. The experimental results show that in the task of detecting vulnerabilities in long smart contracts, this method has better detection capabilities for four types of vulnerabilities: Denial of Service, Reentrancy, Arithmetic, and Timestamp Dependency, compared to the most advanced deep learning smart contract vulnerability detection methods currently available.
Xiongfei Zhao, Hou-Wan Long, Z Li, Jiangchuan Liu · 5 authors
The rapid growth of blockchain and Decentralized Finance (DeFi) has introduced new challenges and vulnerabilities that threaten the integrity and efficiency of the ecosystem. This study identifies critical issues such as Transaction Order Dependence (TOD), Blockchain Extractable Value (BEV), and Transaction Importance Diversity (TID), which collectively undermine the fairness and security of DeFi systems. BEV-related activities, including sandwich attacks, liquidations, transaction replay etc. have emerged as significant threats, collectively generating $540.54 million in losses over 32 months across 11,289 addresses, involving 49,691 cryptocurrencies and 60,830 on-chain markets. These attacks exploit transaction mechanics to manipulate asset prices and extract value at the expense of other participants, with sandwich attacks being particularly impactful. Additionally, the growing adoption of blockchain in traditional finance highlights the challenge of TID, wherein high transaction volumes can strain systems and compromise time-sensitive operations. To address these pressing issues, we propose a novel Distributed Transaction Sequencing Strategy (DTSS) that integrates forking mechanisms with an Analytic Hierarchy Process (AHP) to enforce fair and transparent transaction ordering in a decentralized manner. Our approach is further enhanced by an optimization framework and the introduction of a Normalized Allocation Disparity Metric (NADM) that ensures optimal parameter selection for transaction prioritization. Experimental evaluations demonstrated that the DTSS effectively mitigated BEV risks, enhanced transaction fairness, and significantly improved the security and transparency of DeFi ecosystems. • Distributed Transaction Sequencing Strategy (DTSS) was proposed address TOD, BEV, and TID issues. • DTSS adapts block size based on transaction attributes. • An optimization framework was introduced to determine optimal parameters for DTSS. • Experimental results show the superiority of DTSS in mitigating risks associated with BEV. • Results also show that DTSS can ensure a fair and transparent transaction ordering.
Julio López Fenner, Carlos Castillo-Muñoz, Francisco Escobar, Ana Bustamante-Mora · 5 authors
Privacy-preserving secure multi-party computation protocols are known to face scalability and efficiency challenges in environments where participants hold distinct attributes of the same records (vertical partitioning) or controls a subset of complete records (horizontal partitioning), as in cross-institutional health data analysis or federated IoT analytics, mostly because of communication overhead and the need to address adaptability to large scale or heterogeneous settings. This work introduces a novel MPC protocol based on the Damgård–Jurik cryptosystem and Schnorr zero-knowledge proofs (ZKP), designed to securely aggregate private data distributed across a number of parties. By combining homomorphic encryption with non-interactive ZKP’s, the protocol ensures privacy, correctness, and scalability, aligning with the principles of privacy-enhancing technologies (PETs). Our approach minimizes data exposure, allowing participants to audit results, and achieves linear O(N) communication complexity, thus making it suitable for large-scale applications in secure data analytics and collaborative computing.
Tarek Galal, Valeria Tisch, Katja Assaf, Andreas Polze
Railways provide a critical service and operate under strict regulatory frameworks for implementing changes or upgrades. Despite their impact on the public, these frameworks do not define means or mechanisms for transparency towards the public, leading to reduced trust and complex tracking processes. We analyse the German guideline for railway-infrastructural modifications from proposal to approval, using the guideline as a motivating example for modelling decisions in processes using digital signatures and zero-knowledge proofs. Therein, a verifier can verify that a process was executed correctly by the involved parties and according to specification without learning confidential information such as trade secrets or identities of the participants. We validate our system by applying it to the railway process, demonstrating how it realises various rules, and we evaluate its scalability with increased process complexities. Our solution is not railway-specific but also applicable to other contexts, helping leverage zero-knowledge proofs for public transparency and trust.
Dušan Morháč, Kristián Košťál, Viktor Valaštín, Ivan Kotuliak
Abstract Interoperability is a critical aspect of fluent liquidity and healthy blockchain ecosystems. Achieving seamless interoperability within heterogeneous blockchains often proves challenging due to the inconsistencies in architecture, asset registration and management logic, implementations, and smart contract designs. These unresolved challenges frequently cause fragmented liquidity within ecosystems. This paper proposes a modular and novel solution, UniSpell, that works as a universal adapter to achieve seamless interoperability and overcome fragmented liquidity problems. This is possible by leveraging Polkadot’s native cross-chain protocol, XCMP, allowing developers to implement dApps that can source liquidity, cross-chain transfer, and swap assets within one line of code. UniSpell presents a framework that is easily extendable and applicable to multichain ecosystems outside of its native ecosystem, Polkadot. UniSpell’s primary goal is to cultivate a dynamic ecosystem by enhancing liquidity and encouraging the addition of new assets. This approach enables innovation and broadens the adoption of decentralized finance (DeFi) solutions.
Данная работа посвящена обзору автоматизированных инструментов безопасной разработки смарт-контрактов Ethereum. Рассматриваются актуальные уязвимости, характерные для смарт-контрактов, такие как уязвимость повторного входа, недостаточный контроль доступа, манипуляции с оракулом цены и другие. К каждой уязвимости приведена иллюстрация с уязвимым кодом. Далее рассмотрены разные типы существующих автоматизированных инструментов безопасной разработки смарт-контрактов: статический анализатор, линтер, символьный исполнитель, фаззинг и подходы на основе машинного обучения. Для каждого типа инструмента рассмотрено соответствующее реальное решение, которое является одним из лучших в своей категории. Это такие open-source решения как статический анализатор Slither, линтер Solhint, символьный исполнитель Mythril и фреймворк Foundry, который содержит в себе возможность фаззинга. Также рассмотрена текущая эффективность современных решений, которая показывает, что текущие угрозы плохо детектируется существующими инструментами. Исходя из этого предложены направления для дальнейшего развития новых инструментов безопасной разработки смарт-контрактов. Полученные результаты могут быть использованы для более глубокого понимания вопросов безопасности смарт-контрактов, а также для повышения безопасности децентрализованных приложений и развития методов автоматизированного аудита смарт-контрактов. This paper provides an overview of automated tools for secure development of Ethereum smart contracts. The article discusses current vulnerabilities specific to smart contracts, such as re-entrancy vulnerability, insufficient access control, price oracle manipulation, and others. Each vulnerability is accompanied by an illustration of the vulnerable code. Next, we discuss different types of existing automated tools for secure smart contract development: static analyzer, linter, symbolic executor, fuzzing, and machine learning-based approaches. For each type of tool, a corresponding real solution is considered, which is one of the best in its category. These are open-source solutions such as the Slither static analyzer, the Solhint linter, the Mythril symbolic executor, and the Foundry framework, which includes fuzzing capabilities. The current effectiveness of modern solutions is also considered, which shows that current threats are poorly detected by existing tools. Based on this, directions for the further development of new tools for the secure development of smart contracts are proposed. The obtained results can be used to gain a deeper understanding of smart contract security issues, as well as to enhance the security of decentralized applications and develop automated smart contract auditing methods.
Mrs. S. Sri Sayelakshmi, Randhir Kumar, M Harini, B Oviya
In modern cloud computing environments, data is often stored on cloud servers in the form of ciphertext to ensure security and confidentiality. Access to this encrypted data typically requires a third party to provide an access key to the consumer. However, the existing use of the SHA-256 encryption method has limitations, as it leaves the data vulnerable to tampering. To address this issue, a Proof of Stake (PoS) algorithm is proposed as a more secure alternative. In this approach, data is encrypted using a robust encryption algorithm, and all transactions are recorded on a blockchain using the PoS algorithm. This method not only enhances data security by making tampering more difficult but also ensures the integrity of transactions by securely storing them in blocks. The proposed system offers a more resilient and tamper-resistant solution for cloud data storage and access, managing sensitive information in the cloud. Additionally, it reduces dependency on third-party key providers, further minimizing security risks.
How can the US response to the initial advent of Bitcoin in 2009 be understood? This chapter argues that the first encounters between the US and Satoshi Nakamoto-inspired financial technologies were characterized by persistent bad policy. The federal government’s approach sought to reconcile the need to contain potential national security harms associated with Bitcoin, while encouraging wider applications of its underlying blockchain technology on the other hand. These dual policy objectives succeeded in containing some of the worst potential excesses of Bitcoin-related experimentation. Yet they failed to address the rapid growth of actual consumer harms as scams and frauds proliferated along with environmental harms as a large part of the energy intensive Bitcoin and cryptocurrency production industry located in the US. Efforts at reversing the initial policy have been constrained as the growth of this sector in the US had formed a powerful new lobbying force.
Abstract— The swift uptake of cloud computing services has brought with it new complexities in tracking and billing for resource usage, frequently resulting in disagreements between customers and service providers as a result of unclear pricing models. This study investigates the use of Distributed Ledger Technology (DLT) to improve transparency, trust, and accuracy in cloud resource billing. By taking advantage of the distributed and immutable aspect of distributed ledgers, bill records can be recorded, stored, and audited in real-time by anyone involved in an immutable manner. This removes dependence on centralized bill authorities and reduces tampering and manipulation of the data. Our proposed blockchain framework tracks resource consumption metrics, such as compute time, storage, and bandwidth used, directly on a distributed ledger. Smart contracts eliminate manual billing computations and payments, providing consistency and fairness. With this system, users obtain verifiable information on their billing history, while providers enjoy fewer operational disagreements and higher customer trust. Our paper presents the system architecture, principal technical challenges, possible performance overheads, and feasible solutions for deployment at scale. Finally, this research illustrates how the convergence of distributed ledger systems with cloud billing systems presents a revolutionary entry point to the development of an increasingly open and responsive cloud economy. Keywords— Ledger, Blockchain, Billing , software.
Content-Based Image Retrieval (CBIR) has become a critical technology for efficiently searching and retrieving images from large datasets based on their visual content. Traditional CBIR systems, which rely on low-level features like color, texture, and shape, often struggle with semantic gaps and scalability issues. With the rapid advancements in deep learning and cloud computing, there is a growing need to enhance CBIR performance for real-world applications. In order to tackle the issues, an enhanced CBIR process leveraging advanced neural networks, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and attention mechanisms is proposed. It integrates multimodal data, including text and audio, to improve retrieval accuracy. Additionally, cloud-based infrastructure is employed to support large-scale image processing, enabling faster retrieval times and real-time performance. Edge computing techniques are also incorporated to reduce latency in applications requiring immediate responses. The proposed model demonstrates significant improvements in retrieval accuracy and efficiency compared to traditional CBIR methods. Deep learning models, particularly CNNs with transfer learning and attention mechanisms, effectively capture high-level semantic features. The integration of cloud infrastructure enhances scalability and real-time processing capabilities, while multimodal retrieval improves search relevance. The use of explainable AI techniques adds transparency to the decision-making process, increasing user trust. Hence, the advanced neural networks, coupled with cloud and edge computing, can significantly optimize CBIR systems, making them more robust, scalable, and applicable to a wide range of industries such as healthcare, security, and e-commerce.
Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and (potentially sensitive or private) training data. So-called Zero-knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) would appear to provide the capability to verify model inference without access to such sensitive data. However, applying ZK-SNARKs to modern neural networks, such as transformers and large vision models, introduces significant computational overhead. We present TeleSparse, a ZK-friendly post-processing mechanisms to produce practical solutions to this problem. TeleSparse tackles two fundamental challenges inherent in applying ZK-SNARKs to modern neural networks: (1) Reducing circuit constraints: Over-parameterized models result in numerous constraints for ZK-SNARK verification, driving up memory and proof generation costs. We address this by applying sparsification to neural network models, enhancing proof efficiency without compromising accuracy or security. (2) Minimizing the size of lookup tables required for non-linear functions, by optimizing activation ranges through neural teleportation, a novel adaptation for narrowing activation functions' range. TeleSparse reduces prover memory usage by 67% and proof generation time by 46% on the same model, with an accuracy trade-off of approximately 1%. We implement our framework using the Halo2 proving system and demonstrate its effectiveness across multiple architectures (Vision-transformer, ResNet, MobileNet) and datasets (ImageNet,CIFAR-10,CIFAR-100). This work opens new directions for ZK-friendly model design, moving toward scalable, resource-efficient verifiable deep learning.
Background: Solidity is the primary programming language used for developing smart contracts on Ethereum, representing a new generation of programming languages developed entirely in open environments. Objective: This longitudinal case study examines contribution patterns and emotional dynamics within the Solidity GitHub repository over a ten-year period (2014-2024). Method: We developed a contribution index combining metrics from developer activities (commits, pull requests, comments, and temporal engagement) and applied emotion detection to study communication patterns in a decade-long dataset of developer interactions. Results: The top 1 % of contributors are responsible for around 85 % of project contributions, yet the project exhibits dual paths to prominence: early contributors established technical foundations through code, while later contributors achieved influence through reviews and discussions. Emotional patterns show transitions from initial curiosity and confusion to eventual approval and gratitude. Conclusion: The project's recognition of diverse contribution types and evolving emotional dynamics enables sustainable growth despite concentrated contributions, demonstrating how open-source languages can evolve while maintaining both technical rigor and community engagement.
The water-conducting fracture zone (WCFZ) is a critical geological structure formed by the destruction of overburden during coal mining operations. Accurately predicting the height of the water-conducting fractured zone (HWCFZ) is essential for ensuring safe coal production. Based on more than 150 measured heights of fractured water-conducting zone samples from various mining areas in China, this study investigates the influence of five primary factors on the height: mining thickness, mining depth, length of the panel, coal seam dip, and the proportion coefficient of hard rock. The correlation degrees and relative weights of each factor are determined through grey relational analysis and principal component analysis. All five factors exhibit strong correlations with the height of the fractured water-conducting zone, with correlation degrees exceeding 0.79. Mining thickness is found to have the highest weight (0.256). A multiple nonlinear coordinated regression equation was constructed through regression analysis of the influencing factors. The prediction accuracy was compared with three other predictive models: the multiple nonlinear additive regression model, the BP neural network model, and the GA-BP neural network model. Among these models, the multiple nonlinear coordinated regression model was found to achieve the lowest error rate (7.23%) and the highest coefficient of determination (R2 = 87.42%), indicating superior accuracy and reliability. The model’s performance is further validated using drill hole data and numerical simulations at the B-1 drill hole in the Fuda Coal Mine. Predictive results for the entire Fuda Coal Mine area indicate that as the No. 15 coal seam extends northwestward, the height of the fractured water-conducting zone increases from 52.1 m to 73.9 m. These findings have significant implications for improving mine safety and preventing geological hazards in coal mining operations.
Abstract. This study investigates the transformative potential of blockchain technology in optimizing business processes, digital marketing, and achieving sustainable development goals within the context of global digitalization and increasing consumer demands for transparency. The research employs dialectical methods of cognition, systematic approaches, and analysis-synthesis methodologies to examine blockchain's key advantages including transparency, data security, and automation through smart contracts. The investigation reveals that blockchain technology addresses critical challenges in digital marketing, particularly advertising fraud, which cost the industry approximately $84-140 billion globally in 2024. Invalid traffic (IVT) reached 23% of all mobile advertising impressions, with Ukraine experiencing fraud rates as high as 50.31%. The study demonstrates how blockchain's immutable ledger system can verify ad views and clicks, eliminating fraudulent activities while ensuring transparent budget allocation. Key findings highlight blockchain's capacity to revolutionize supply chain management through real-time product tracking from origin to consumer. Case studies include Walmart's product tracking system, H&M's collaboration with VeChain platform for clothing authenticity verification, and De Beers' diamond supply chain transparency initiative. The research identifies IBM Food Trust as exemplifying blockchain's role in reducing food waste and ensuring safety through transparent record-keeping. The study proposes strategic frameworks for integrating blockchain into business and marketing practices aligned with sustainable development principles. Smart contracts enable automated insurance payouts, peer-to-peer energy trading, and decentralized loyalty programs through tokenization. Environmental applications include carbon credit markets, green financing transparency, and anti-greenwashing certification systems. Despite significant advantages including decentralization, transparency, security, and intermediary elimination, implementation challenges persist: energy consumption, scalability limitations, technical complexity, and regulatory uncertainty. The research concludes that blockchain fosters trust, optimizes resource management, and supports ethical practices, enabling enterprises to achieve long-term competitiveness while contributing to sustainable development objectives.
Sangharatna Godboley, P. Radha Krishna, Sunkara Sri Harika, Pooja Varnam
We propose and develop a framework for validating smart contracts derived from e-contracts. The goal is to ensure the generated smart contracts fulfil all the conditions outlined in their corresponding e-contracts. By confirming alignment between the smart contracts and their original agreements, this approach enhances trust and reliability in automated contract execution. The proposed framework will systematically compare and validate the terms and clauses of the e-contracts with the logic of the smart contracts. This validation confirms that the agreement is accurately translated into executable code. Automated verification identifies issues between the e-contracts and their smart contract counterparts. This proposed work will solve the problems of gap between legal language and code execution, this framework ensures seamless integration of smart contracts into the existing legal framework.
Blockchain technology establishes trust among participants through technical means. However, some malicious nodes may compromise this trust through short-range reorganization attacks for their interest. This paper develops an agent-based model to systematically analyze Proof-of-Stake short-range reorganization attacks, where three types of agents interact through distributed consensus mechanisms with ex-ante, fine-grained, and ex-post reorganization attack strategies. Through rigorous simulation of agent decision-making dynamics, we identify that: (1) Compared with ex-ante reorganization, the ratio of malicious nodes required for ex-post reorganization is much larger. (2) Increasing the node number increases the difficulty of ex-ante and ex-post reorganization. (3) The number of nodes affects ex-post reorganization attacks more significantly than ex-ante attacks. (4) Fine-grained reorganization significantly reduces attack difficulty
Abstract - In the rapidly advancing digital era, the requirement for secure, transparent, and reliable data-sharing mechanisms has become increasingly critical across various sectors. Traditional centralized data-sharing models suffer from inherent limitations, including vulnerability to data breaches, unauthorized access, single points of failure, and insufficient transparency in data access and audit trails. These challenges compromise not only the confidentiality and integrity of sensitive data but also erode stakeholder trust in digital systems. To overcome these issues, this paper presents BlockShare, a blockchain-powered, decentralized framework designed to facilitate secure, tamper-proof, and efficient data exchange. BlockShare leverages the foundational principles of blockchain technology—namely decentralization, immutability, and transparency—to enhance the robustness and reliability of data-sharing architectures. The proposed system eliminates central authority dependence by distributing data storage and control across a decentralized ledger, thereby minimizing potential attack vectors and ensuring continuous data availability. To regulate data access and maintain policy enforcement, smart contracts written in Solidity are integrated within the system. These smart contracts autonomously manage permissions and user authentication, ensuring that only verified and authorized parties can access specific datasets, with every action recorded immutably on the blockchain. Moreover, data confidentiality is preserved through the implementation of AES-256 encryption, a widely recognized standard for high-security data protection. Prior to storage, all data is encrypted and then uploaded to a decentralized file system, specifically the InterPlanetary File System (IPFS), which provides enhanced fault tolerance, redundancy, and distributed access. This dual-layered approach—combining blockchain for governance and IPFS for storage—ensures that data remains protected both in transit and at rest. By integrating smart contract-based automation, robust encryption protocols, and distributed storage solutions, BlockShare delivers a scalable and resilient infrastructure for data exchange. The system is particularly applicable in domains requiring stringent data protection and transparency, such as healthcare, finance, legal, and government sectors. Through this innovative approach, BlockShare aims to redefine trust in digital interactions and lay the groundwork for the next generation of secure data-sharing ecosystems. Keywords - Blockchain, Data Sharing, Decentralized Storage, Smart Contracts, Encryption, IPFS, Ethereum, Security, Data Privacy, AES-256, Web3, Authentication, DApp, Decentralization, Access Control.
Hassen Louati, Ali Louati, Elham Kariri, Abdulla Almekhlafi
Blockchain technology has transformed modern digital ecosystems by enabling secure, transparent, and automated transactions through smart contracts. However, the increasing complexity of these contracts introduces significant challenges, including high computational costs, scalability limitations, and difficulties in detecting anomalous behavior. In this study, we propose an AI-based optimization framework that enhances the efficiency and security of blockchain smart contracts. The framework integrates Neural Architecture Search (NAS) to automatically design optimal Convolutional Neural Network (CNN) architectures tailored to blockchain data, enabling effective anomaly detection. To address the challenge of limited labeled data, transfer learning is employed to adapt pre-trained CNN models to smart contract patterns, improving model generalization and reducing training time. Furthermore, Model Compression techniques, including filter pruning and quantization, are applied to minimize the computational load, making the framework suitable for deployment in resource-constrained blockchain environments. Experimental results on Ethereum transaction datasets demonstrate that the proposed method achieves significant improvements in anomaly detection accuracy and computational efficiency compared to conventional approaches, offering a practical and scalable solution for smart contract monitoring and optimization.
The focus of this research is to identify the factors that make investors victims of fraud in the context of NFT investments. The theoretical foundation used in this study is the Lifestyle Exposure Theory, which helps in understanding how investors' lifestyles influence their risk of being scammed. This research employs a qualitative approach with a case study method through interviews to gain an in-depth understanding of fraudulent patterns in NFT projects. Data analysis is conducted using a descriptive approach to thoroughly illustrate and explain the phenomenon, providing insights into the factors that make investors vulnerable to fraud in the context of NFT investments. Lack of self-control and minimal verification of information make investors more susceptible to fraudulent schemes. The habitual factors identified above demonstrate their role as victims in fraud schemes, a concept known as Participating Victims.
Daniel Hindemburg de Miranda Marques, Dalton Cézane Gomes Valadares
5G is the most recent technology standard for cellular networks, and one of its key elements is the Radio Access Networks (RAN), which furthers the enabling of the 5G basic capabilities: enhanced Mobile Broadband (eMBB), Massive Machine-Type Communication (mMTC), and Ultra-Reliable, Low-Latency Communication (URLLC). To meet the capabilities required by 5G use cases, 5G is distributed, virtualized, and architecturally more complex than previous generations. These capabilities bring benefits but introduce risks and security challenges that must be addressed through controls designed to support and secure 5G services across any operator cloud. Therefore, this paper focuses on studying and evaluating security mechanisms used in RANs. Special attention is given to Distributed Ledger Technologies (DLTs) since they are one of the most studied topics regarding security enhancement. DLTs could bring advantages for improving network security through encryption to protect the information and automate verification and execution of transactions. For this reason, we carried out a systematic review, extracting and analyzing data from 39 papers from 2010 to 2023. Our main results list RAN-related susceptible security dimensions, vulnerabilities, and possible attacks and threats. We also show how DLTs can enhance RANs and present other considered mechanisms to increase RAN security. • The evolution of mobile communication based on openness, softwarization, and virtualization inserts new vulnerabilities into networks. • The increasing number of connected devices, especially IoT ones, is a security attention point in mobile networks. • Various security mechanisms, including Distributed ledger technologies (DLT), may enhance RAN security once these technologies can increase system resilience. • Other security approaches may also address RAN security issues.