In smart contract fuzz testing, it is crucial to consider the inter-dependencies between the contract functions. To effectively test the business logic of a contract, its functions must be invoked in a meaningful order. In this paper, we propose techniques that utilize static analysis on Ethereum bytecode to tackle this challenge. When compared with the current state-of-the-art, our approach takes Solidity compiler’s variable packing optimization into account and allows more precise analysis of the data-flows between functions. In addition, we devise a novel test case initialization algorithm for fuzz testing, which minimizes the redundancy in the generated seed set. Our algorithm reduces test cases that share similar function call patterns and leads to more effective testing of the contract code during the fuzz testing. Experimental results show that the proposed techniques improve the effectiveness of smart contract fuzz testing for vulnerability detection. Specifically, our techniques enabled the fuzz testing tool to trigger the target bugs in the benchmark 3.0 times faster on average.
Abdul Khalique Shaikh, Naresh Adhikari, Amril Nazir, Abdul Salam Shah · 6 authors
<ns3:p>Background Ensuring the security and trustworthiness of a digitized and automated electoral process remains a significant challenge in democratic systems. As digital voting systems are increasingly being investigated around the world, ensuring the integrity of the process using robust security measures is of great importance. This paper presents a simplified model to enhance electoral integrity by leveraging Blockchain technology in the context of Oman’s digital voting system. The model uses Blockchain technology to create a secure and trustworthy voting environment, addressing key vulnerabilities in digital electoral systems. Methods The research utilized a quantitative approach, employing an experimental design methodology using open-source software to simulate voting systems. Synthetic population data is utilized for operating these systems, while advanced biometric authentication technologies are used to verify voter identities. Blockchain technology is leveraged to ensure secure vote recording, with smart contracts used to authenticate voters and securely record votes. Additionally, synchronous transactions are executed for both voter registration and voting processes, enhancing the overall security and efficiency of the system. Results The experimental results shows that Blockchain enhances electoral integrity and security in Oman voting system, improves transparency and reliability in elections. The performance evaluation of the model focuses on efficiency, reliability, and scalability metrics. Asynchronous transactions are utilized to improve processing time for voter registration and voting. Election administrators can manage, monitor, and certify election results, while Ethereum nodes ensure decentralized verification and transparency in the voting process. Conclusion This research offers insights for policymakers to consider Blockchain for electoral reforms, addressing issues like data integrity, fraud prevention, and transparency to boost voter trust. A strong regulatory framework and public awareness are crucial for successful implementation. Pilot projects are needed to assess Blockchain’s practical impact. Oman could lead global innovation in electoral technology, though infrastructure and public resistance challenges must be managed.</ns3:p>
Md. Sameeruddin Khan, Tom Chen, Mithileysh Sathiyanarayanan, Mohammed Mujeerulla · 5 authors
The Internet of Things (IoT) model is presented in this paper with multi-layer security based on the Lenstra-Lenstra-Lovasz (LLL) algorithm. End nodes for the Internet of Things include inexpensive gadgets like the Raspberry Pi and Arduino boards. It is not practical to run rigorous algorithms on them, as opposed to computer systems. Therefore, a cryptography procedure is required that could function on this IOT equipment. Bitcoins and Ethereum are examples of cryptocurrency and Ripple employs techniques such as elliptic curve digital signature, Elliptic-Curve Diffie-Hellman (ECDH), and algorithm to sign any cryptocurrency on SECP256k1 elliptic curves transactions. By using Lenstra-Lenstra-Lovasz on a real-world Bitcoin blockchain and applying it to multiple dimensions, such as nonce leakage and weak nonces across several elliptic curves with different bit sizes on a Raspberry Pi, we can demonstrate the security of elliptic curve cryptosystems. Public key encryption techniques are seriously threatened by the development of quantum computing. Therefore, employing lattice encryption with Nth Degree Truncated Polynomial Ring Units (NTRU-NTH) on the Bitcoin blockchain will increase the resistance of Bitcoin blocks to quantum computing assaults. The execution time taken on SECP256k1 is 131.7 Milli seconds comparatively faster than NIST-224P and NIST-384P.
Blockchain technology transforms banking by solving issues in international money transfers. The decentralized blockchain system prevents tampering and removes intermediaries to speed up transactions and lower costs while protecting data integrity. Our investigation focuses on how blockchain prevents financial fraud while allowing users to see transaction data in real-time and making international financial systems work better. This research analyzes how blockchain works in financial transactions and shows its effects on payment times, cost savings, and protection against fraud through data studies. Despite demonstrating powerful disruption blockchain struggles with processing delays and lacks defined standards while integrating technology proves challenging. This research explores why organizations find it difficult to use blockchain technology and suggests methods that can help to use blockchain effectively in daily operations. This research examines how blockchain technology affects global finance operations and shows its benefits for international payments while explaining its potential to transform worldwide financial systems.
This study explores the integration of technical indicators, specifically Exponential Moving Averages (EMA) and Volume Weighted Average Price (VWAP), into machine learning models for cryptocurrency price forecasting. Our findings reveal that including these indicators can complicate the modeling process without necessarily improving performance. Support Vector Regression (SVR) and Random Forest Regressor (RFR) models outperform deep learning approaches such as Long Short-Term Memory (LSTM) networks, demonstrating higher predictive accuracy with simpler feature sets. These findings emphasize the challenges of high-dimensional data and the critical role of rigorous feature selection and preprocessing in financial forecasting. Practical implications and trade-offs between model complexity and prediction accuracy are discussed, providing valuable insights for researchers and practitioners in financial analytics.
The paper investigates how attention to the Russia-Ukraine war affects cryptocurrency returns by creating a Google search volume index (GSVI) using Google trends keywords. It finds that crypto returns react positively to attention to war and negatively to the volatility index (VIX), demonstrating that investor fear during times of crisis may increase interest in cryptocurrencies. The research provides specific insights into crypto markets that can aid portfolio managers and regulators. It also adds to the limited studies on the impact of war on cryptocurrency returns.
FinTech innovations have reshaped the financial sector, enabling greater security, accessibility, and efficiency. The integration of advanced technologies such as blockchain, artificial intelligence (AI), machine learning, and big data analytics has revolutionized financial services, improving operational efficiency and fostering financial inclusion. These innovations provide secure, seamless, and real-time transactions, reducing dependency on traditional banking systems while promoting a more customer-centric approach. This paper examines the role of FinTech in promoting sustainable finance through digital financial solutions, regulatory compliance, and enhanced security. The emergence of digital lending platforms, robo-advisors, decentralized finance (DeFi), and digital payment systems has transformed how individuals and businesses interact with financial services. By leveraging automation and data-driven decision-making, FinTech facilitates transparent and cost-effective financial services, addressing inefficiencies in traditional models. Furthermore, the study explores market trends, risk management strategies, and future developments in the FinTech industry, providing insights into the evolution of financial technologies and their implications for sustainability. With increasing regulatory scrutiny, cybersecurity concerns, and the growing need for responsible investing, the intersection of FinTech and sustainable finance has become crucial. This paper highlights how FinTech fosters green finance initiatives, enhances ESG (Environmental, Social, and Governance) investments, and contributes to a more resilient and inclusive financial ecosystem. Keywords: FinTech, Sustainable Finance, Digital Financial Solutions, Regulatory Compliance, Financial Security, Market Trends, Risk Management, Blockchain, AI.
Abstract Centrally administrated systems have historically facilitated inter-organizational data exchange in supply chains (SC), relying on the message standard electronic data interchange (EDI). However, the current use of EDI fails to meet information needs, as point-to-point interfaces complicate information sharing among multiple partners and batch processing lacks real-time capabilities. This results in information asymmetries, leading to inefficiencies. Distributed ledger technology (DLT), which offers decentralized communication and data storage, presents a potential solution. In this paper, we present a systematic literature review comparing the centralized architectures utilizing EDI applications with the decentralized architecture of DLT within SCs. We identified the limitations of the current systems and assessed whether DLT offers a solution. The findings show that DLT enhances real-time data exchange, automation potential, and transparency, but also faces shortcomings. Integrating EDI with DLT offers a promising approach to leverage synergies and address the weaknesses of both technologies, e.g., lacking standards for DLT.
This paper introduces Web 3.0 NEXT , a network design that pushes Web3 decentralization even further by reducing the dependency on centralized or traditional internet service providers (ISPs) and data centres. By integrating peer-to-peer mesh networking, decentralized storages, blockchain-based authentication and verification system, and a suite of emerging off-grid connectivity technologies like Wi-Fi mesh, LoRaWan, and satellite networks, the suggested system seeks to establish a strong, self-sustaining network infrastructure. This paper explores the benefits and technical challenges of such a system. That area can be used in a disaster-prone area, national security and economic innovations, which may force centralized authority from suppressing it. We will also discuss ethical, legal, financial, energy, user adoption, and security considerations, alongside relevant case studies and provide a holistic view of the potential challenges faced in deploying WEB 3.0 NEXT.
In Federated Deep Learning (FDL), multiple local enterprises are allowed to train a model jointly. Then, they submit their local updates to the central server, and the server aggregates the updates to create a global model. However, trained models usually perform worse than centralized models, especially when the training data distribution is non-independent and identically distributed (nonIID). NonIID data harms the accuracy and performance of the model. Additionally, due to the centrality of federated learning (FL) and the untrustworthiness of enterprises, traditional FL solutions are vulnerable to security and privacy attacks. To tackle this issue, we propose FedAnil, a secure blockchain enabled Federated Deep Learning Model that improves enterprise models decentralization, performance, and tamper proof properties, incorporating two main phases. The first phase addresses the nonIID challenge (label and feature distribution skew). The second phase addresses security and privacy concerns against poisoning and inference attacks through three steps. Extensive experiments were conducted using the Sent140, FashionMNIST, FEMNIST, and CIFAR10 new real world datasets to evaluate FedAnils robustness and performance. The simulation results demonstrate that FedAnil satisfies FDL privacy preserving requirements. In terms of convergence analysis, the model parameter obtained with FedAnil converges to the optimum of the model parameter. In addition, it performs better in terms of accuracy (more than 11, 15, and 24%) and computation overhead (less than 8, 10, and 15%) compared with baseline approaches, namely ShieldFL, RVPFL, and RFA.
Seelammal Chinnaperumal, Sekar Kidambi Raju, Amal H. Alharbi, Subhash Kannan · 8 authors
The research is aimed at filling the gap regarding the development of long-lasting, secure technologies that help build decentralized systems. Other consensus models, such as the Proof of Work (PoW), prevailing in cryptocurrencies, are known to be expensive in terms of energy, hence the development of enlightened models like Proof of Lightweight Hash, whereby while developing the model, an emphasis is placed on energy efficiency without compromising on security. At the same time, new technologies such as battery storage and electric vehicles are disrupting consumer habits where renewable energy is favored, and a decentralized energy market is promoted. It hails the aspect of fine access control provided by blockchain in addition to decentralization; a permission system is vital for any entities that require strict access control due to the nature of the data they hold. Blockchain in IoT and AI makes strategies innovative, adaptable, large-scale, and inclusive to make unique changes that benefit different industries and need scalability. Due to this combining of energy innovations and digital technologies, both energy and data networks become nearer to consumers, advocating sustainable, efficient urbanism. Altogether, these improvements will lead toward the emergence of systems that, aside from being technologically innovative, are also environmentally sustainable and protected. So the interaction of technology, ecological stability, and viable security provides the basis for a cleaner, stronger, de-centralized future as applied to advanced technologies, thus inculcating an equilibrium and stronger society.
The proliferation of cryptocurrency throughout society has led to widespread usage within criminal offending. Despite this, limited research has investigated fear of cryptocurrency-based victimization, or the role that financial or cryptocurrency literacy play in influencing such fears. This study examines the influence in which financial and cryptocurrency literacy plays in both personal and altruistic fear of financial and cryptoeconomic crimes. Using a sample of college undergraduates (n = 433), and employing a validated scale of cryptocurrency literacy, results indicate that cryptocurrency literacy is not significantly associated with all modalities of fear of crime investigated, with the exception of altruistic fear of non-cryptocurrency financial crimes. Conversely, general financial literacy was negatively associated with personal fear of both financial crimes and non- cryptoeconomic financial crimes. Findings are discussed in light of research and policy implication as well as limitations.
This article explores the transformative potential of blockchain technology in maintaining test data integrity across regulated industries, particularly in finance, healthcare, and pharmaceuticals. The article examines how blockchain's inherent characteristics address critical challenges in data security, compliance, and operational efficiency. Through analysis of implementation strategies, industry-specific applications, and emerging trends, the article demonstrates how blockchain technology revolutionizes test data management through immutable record-keeping, decentralized architecture, and smart contract automation. The article indicates significant improvements in data security, compliance management, and operational efficiency across all examined sectors, while highlighting the importance of structured implementation approaches and industry-specific considerations.
The evolution of regulatory frameworks in the FinTech industry presents both opportunities and challenges as organizations navigate an increasingly complex compliance landscape.From digital payments to decentralized finance, FinTech's transformative impact on global financial services necessitates robust regulatory oversight while maintaining innovation.
This article offers ‘stages’, an original device, to sharpen the focus on a particular divinatory economic performance: the folding of imagined profitable futures into the present to create the impression that profitable futures are imminent or already realized. Drawing on ethnographic material from the startup and multi‐level marketing (MLM) cryptocurrency sectors, and utilizing ‘stages’ as a concept/pun – in the spatial and temporal sense – I show how economic performances must be ‘staged’ to convince people to invest. ‘Staging’ economic performances leads to creating a physical space of heightened excitement, expertise, and a temporal period that is experienced as urgent where one must think in an innovative way to bring about a profitable future. Under these conditions, ‘stages’ reveals how the line between a legitimate and an illegitimate project becomes indistinguishable to those on the ground. Doing your ‘due diligence’ to discern between the two is anything but straightforward. Despite claims by Euro‐American financial elites that startups and MLMs are polar opposites, this article demonstrates their striking similarity. More broadly, ‘stages’ reveals how economic performances gain efficacy, travel beyond specific sites, and come to act on people.
This review paper aims to examine the key challenges associated with wastewater management in major cities of the East African region and explore the emerging opportunities for addressing this crisis. An extensive database search identified 100 peer-reviewed publications related to wastewater crisis in East Africa. The reviewed literature was analysed and synthesized to develop an understanding of the topic. The findings from this review have shown that less than 55% of the East African population (30% in Tanzania, 40% in Uganda, 50% in Kenya, 40% in Rwanda, 20% in Burundi, 30% in Ethiopia, 50% in Sudan, and 15% in Somalia) is connected to sewers systems. Additionally, the study has revealed the following issues as the main challenges facing wastewater management in East Africa; poor infrastructure, regulatory and institutional deficiencies, poor financing and cost recovery mechanism, and poor community participation and utility management, which in turn results to potential environmental and public health implications. This paper has also identified possible innovative strategies and emerging opportunities for sustainable wastewater management. These include; decentralized wastewater treatment systems, resource recovery and reuse, public–private partnerships, policy and governance reforms and the use of modern technologies such as membrane filtration, advanced oxidation processes, and electrochemical treatment methods. In addition, the role of policy and governance reforms in enabling sustainable wastewater management in East Africa has also been emphasized, in this paper. The findings of this review emphasize the urgent need for comprehensive policies, investments, and collaborative efforts to address the wastewater crisis and harness the potential benefits of wastewater as a resource.
Yin, Chaoyue, Mingzhe Li, Jin Zhang, Lin You · 6 authors
With the development of Ethereum, numerous blockchains compatible with Ethereum's execution environment (i.e., Ethereum Virtual Machine, EVM) have emerged. Developers can leverage smart contracts to run various complex decentralized applications on top of blockchains. However, the increasing number of EVM-compatible blockchains has introduced significant challenges in cross-chain interoperability, particularly in ensuring efficiency and atomicity for the whole cross-chain application. Existing solutions are either limited in guaranteeing overall atomicity for the cross-chain application, or inefficient due to the need for multiple rounds of cross-chain smart contract execution. To address this gap, we propose IntegrateX, an efficient cross-chain interoperability system that ensures the overall atomicity of cross-chain smart contract invocations. The core idea is to deploy the logic required for cross-chain execution onto a single blockchain, where it can be executed in an integrated manner. This allows cross-chain applications to perform all cross-chain logic efficiently within the same blockchain. IntegrateX consists of a cross-chain smart contract deployment protocol and a cross-chain smart contract integrated execution protocol. The former achieves efficient and secure cross-chain deployment by decoupling smart contract logic from state, and employing an off-chain cross-chain deployment mechanism combined with on-chain cross-chain verification. The latter ensures atomicity of cross-chain invocations through a 2PC-based mechanism, and enhances performance through transaction aggregation and fine-grained state lock. We implement a prototype of IntegrateX. Extensive experiments demonstrate that it reduces up to 61.2% latency compared to the state-of-the-art baseline while maintaining low gas consumption.
The technical architecture establishes a multi-node data certification platform via consortium blockchain, integrates the InterPlanetary File System (IPFS) for distributed encrypted storage of experimental data, and automates ethical reviews, experimental protocol supervision, and resource allocation through smart contracts. Innovatively, the system mints NFT-based digital identity certificates with unique digital fingerprints to comprehensively document genetic profiles, experimental histories, and medical records of individual mice, ensuring verifiability and tamper-resistance of full-lifecycle data.At the governance level, a token-based economic model and decentralized autonomous organization (DAO) framework are introduced. Dynamic incentive mechanisms promote secure cross-institutional research data sharing, while on-chain voting protocols enable decentralized scientific decision-making, effectively balancing open data access with privacy protection requirements. This system establishes a trusted data infrastructure spanning "biological individuals-experimental processes-research outcomes," providing a scalable Web 3.0 paradigm for digital transformation in life sciences. It drives the evolution of laboratory animal management toward intelligent, standardized, and ethics-compliant practices.
Ethereum has adopted a rollup-centric roadmap to scale by making rollups (layer 2 scaling solutions) the primary method for handling transactions. The first significant step towards this goal was EIP-4844, which introduced blob transactions that are designed to meet the data availability needs of layer 2 protocols. This work constitutes the first rigorous and comprehensive empirical analysis of transaction- and mempool-level data since the institution of blobs on Ethereum on March 13, 2024. We perform a longitudinal study of the early days of the blob fee market analyzing the landscape and the behaviors of its participants. We identify and measure the inefficiencies arising out of suboptimal block packing, showing that at times it has resulted in up to 70% relative fee loss. We hone in and give further insight into two (congested) peak demand periods for blobs. Finally, we document a market design issue relating to subset bidding due to the inflexibility of the transaction structure on packing data as blobs and suggest possible ways to fix it. The latter market structure issue also applies more generally for any discrete objects included within transactions.
Ineffective Policies - Causes and Consequences of Bad Policy Choices; Using cutting-edge research and analysis, this book states the case for studying ineffective policies, demonstrates their harmful effects across policy fields and provides policy makers with the tools to reflect, identify and act upon them.
The urgency of this research is to increase efficiency, transparency and sustainability in increasingly complex and challenging agribusiness supply chains. The aim of this research is to develop an integrated system that combines IoT capabilities in collecting agricultural data in real-time, AI to analyze data and provide recommendations for action, as well as security and transparency guaranteed by blockchain technology. The method used is a mixed methods approach, this approach combines qualitative and quantitative elements to obtain a deeper understanding. A qualitative approach is used to gain a contextual perspective, while a quantitative approach is used to measure performance empirically. This research uses a case study design on a sensor-based agricultural monitoring system because of its ability to provide in-depth and holistic insights. The research population consists of users and stakeholders in sensor-based agricultural monitoring systems. The sample was selected purposively to cover various aspects of the supply chain. Data was collected through in-depth interviews, direct observation, surveys of system users and collection of sensor and transaction data from agricultural monitoring systems. The research results show that the integration of IoT, AI, and blockchain significantly improves operational efficiency in agribusiness supply chains. Implementation of this integrated system resulted in an increase in productivity of up to 22%, a reduction in pesticide use by 35%, an increase in water use efficiency by 30%, and a reduction in operational costs by 18%. Statistical analysis confirmed a strong positive correlation between the use of integrated technology and increased operational efficiency (R=0.85, p<0.01).
In view of the problems of false property rights and difficulties in identity authentication in intellectual property transactions, an identity authentication model for intellectual property transactions based on an alliance chain is proposed. Firstly, the two-factor identity authentication model's roles, functions, and processes are constructed. Secondly, the two-factor authentication mechanism of ID password combined with physiological and property rights features is proposed, the identity identification generation method of fingerprint biometrics and intellectual property features is established, and the constraint compression strategy based on Poseidon hash is designed to reduce the workload of zero-knowledge proof algorithm and realize the consistency of property rights identity. Finally, the security and performance analysis of the authentication model is carried out, and the comparison and validation of related models are carried out, which shows that the model has good security and reliability.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The integration of cloud computing and distributed ledger technology (DLT) isrevolutionizing energy derivatives trading by transforming traditional market structures and operational workflows.The adoption of these technologies addresses key challenges in trade lifecycle management, market transparency, and regulatory compliance while introducing new possibilities for automation and efficiency.Through innovative architectures and smart contract implementations, market participants benefit from enhanced security, reduced settlement times, and improved risk management capabilities.The convergence of cloud infrastructure with DLT platforms creates a robust foundation for next-generation trading systems, offering opportunities for market evolution while presenting technical challenges that require careful consideration and strategic solutions.
The Ethereum blockchain operates as a decentralized platform, utilizing blockchain technology to distribute smart contracts across a global network. It enables currency and digital value exchange without centralized control. However, the exponential growth of online commerce has created a fertile ground for a surge in fraudulent activities such as money laundering and phishing, thereby exacerbating significant security vulnerabilities. To combat this, our article introduces an ensemble learning approach to accurately detect fraudulent Ethereum blockchain transactions. Our goal is to integrate a decision-making tool into the decentralized validation process of Ethereum, allowing blockchain miners to identify and flag fraudulent transactions. Additionally, our system can assist governmental organizations in overseeing the blockchain network and identifying fraudulent activities. Our framework incorporates various data pre-processing techniques and evaluates multiple machine learning algorithms, including logistic regression, Isolation Forest, support vector machine, Random Forest, XGBoost, and recurrent neural network. These models are fine-tuned using grid search to enhance their performance. The proposed approach utilizes an ensemble of three distinct models (Random Forest, extreme gradient boosting (XGBoost), and support vector machine) to further improve classification performance. It achieves high scores of over 98% across key classification metrics like accuracy, precision, recall, and F1-score. Moreover, the approach is suitable for real-world usage, with an inference time of 0.13 s.