The overall objective of the study is to understand the current status of blockchain applications and evaluate their ability to satisfy the growing demand for blockchain knowledge in the applications industry.In order to determine which would be the superior option in each situation, it also evaluated the respective advantages of Ethereum and Hyperledger.The study's extensive data set allowed it to offer priceless insights into the intricate workings of a blockchain application.The materials used included reports, journals, and periodicals.The "smart contract" refers to a digital transaction that runs on its own, logs the pertinent dynamic activity on a distributed ledger, and uses predefined criteria to demonstrate its legitimacy.The key component of a blockchain that enables its use as a platform for use cases beyond currency is a smart contract.Voting, education, entertainment, real estate, the Internet of Things (IoT), The development of blockchain technology has advanced significantly in recent years, with a particular emphasis on smart contracts; yet, little research has been done on the idea.Notwithstanding the many advantages of smart contracts, a number of obstacles have prevented their widespread use, including as security holes, coverage gaps, and the difficulties of lawfully enforcing contracts.
The integration of Internet of Things (IoT) devices into smart environments has become increasingly prevalent, resulting in the collection of valuable user and service data. However, effectively utilizing this data often requires its aggregation on a central server to train algorithms capable of identifying and preventing malicious attacks, such as reconnaissance, DoS (Denial of service), DDoS (Distributed denial of service) within IoT networks. This transmission of raw data not only incurs substantial bandwidth costs but also raises significant privacy concerns. In this paper, we propose a federated learning framework for intrusion detection on IoT networks that incorporates a distributed storage system based on the Ethereum blockchain, enhancing the security of the federated learning process. This design offers several key benefits, including scalability, high availability, redundancy, and the capacity to process large datasets. Despite these advantages, relying solely on federated learning may not yield accurate results, particularly when dealing with highly imbalanced datasets. To address this challenge, we have integrated a diffusion model for data augmentation at each local node, which strengthens model robustness. Furthermore, to protect data privacy at each local node, we utilize transmitting and averaging model parameters instead of raw data. The proposed framework is trained and evaluated in two datasets. The MNIST (Modified National Institute of Standards and Technology) dataset and BoT-IoT dataset. Our results indicate significant improvements in detecting zero-day attacks, achieving an average F1-score of 98.3% on the short version of the BoT-IoT dataset as well.
Abstract This paper quantitatively analyses the development status and market share of cryptocurrencies by collecting relevant information and explores the correlation between the cryptocurrency market and the performance of China’s financial market and financial market pressure through the correlation analysis method. Using VAR model impulse analysis to portray the dynamic relationship between cryptocurrencies and the financial market during unexpected events can help show the risk changes of the cryptocurrency market more intuitively. The analysis shows that cryptocurrencies have entered a stage of explosive development, and by 2023, their overall market value will reach about $3 trillion. Among them, Bitcoin has a market share of 39.8%. The correlation coefficients of Bitcoin, Litecoin, Ethereum, and Ripple with the Chinese financial market are -0.0138, −0.0225, −0.0114, and −0.0143, which are negatively correlated. There is a correlation between cryptocurrencies and the impact of market volatility.
Hasib Shamshad, Fasee Ullah, Syed Adeel Ali Shah, Muhammad Faheem · 5 authors
Cryptocurrencies have reshaped finance with secure, decentralized trading, attracting investor interest due to high volatility and potential returns. Accurate price forecasting is essential for optimizing returns and managing risks in digital markets. This study introduces OPTICALS, a novel framework for daily cryptocurrency price forecasting, focusing on transparency, robust performance assessment, and interpretability in machine and deep learning models. Unlike existing methods, OPTICALS provides detailed insights into model predictions by optimizing hyperparameters and identifying each model’s strengths and limitations. The framework evaluates five models-XGBoost, LightGBM, LSTM, Bi-LSTM, and GRU-on three major cryptocurrencies: Ethereum, Binance, and Solana, known for high trading volumes and distinct characteristics. OPTICALS incorporates a “Look-back window” hyperparameter, using recent historical prices to predict next-day trends through Moving Averages analysis. This parameter refines lagged feature engineering to enhance trend capture and predictive accuracy. Models underwent rigorous evaluation, including multiple simulations and hyperparameter tuning. Gradient Boosting models were tuned via GridSearchCV and regularization to improve performance through diverse ensembles. RNN models were optimized by adjusting neurons, stacks, epochs, batch sizes, and optimizers. Predictions were validated against one-week-ahead prices to ensure robust accuracy. Findings show that GRU and XGBoost excel at predicting real-time trends, with GRU supporting day trading and XGBoost benefiting swing trading. This study advances cryptocurrency analytics, providing practical forecasting tools for traders, investors, and institutions to navigate volatility and manage risks effectively.
Manjula K. Pawar, Prakashgoud Patil, D. G. Narayan, Vasundhara Pandey · 6 authors
Blockchain’s decentralized, transparent, and immutable nature has revolutionized digital transactions by removing the need for central authorities. Ethereum stands out among blockchain platforms for facilitating secure peer-to-peer transactions via smart contracts. Despite its transformative potential, blockchain faces challenges, particularly with the PoW consensus algorithm, which demands high energy consumption and raises centralization concerns. This affects the scalability of Blockchain by reducing the throughput. This paper explores machine learning (ML) integration to address these challenges, specifically focusing on optimizing miner selection in the Ethereum blockchain based on predicted transaction times. The study compares the performance of various machine learning models, including ElasticNet, Lasso Regression, Multilayer Perceptron (MLP) Regression in optimizing miner selection for reduced transaction times on the Ethereum blockchain. This study advances the ongoing research on integrating machine learning with blockchain to address the shortcomings of traditional Proof of Work (PoW) systems. It emphasizes the potential of machine learning to propel future innovations in blockchain technology.
Rojalina Priyadarshini, Rhishav Pandey, K C Ankit, Deepesh Bhandari · 7 authors
Verifying the legitimacy of original documents such as educational degree certificates is crucial. If these are found to be fraudulent, it can cause significant disruptions in the hiring process, resulting in substantial productivity losses. The researchers suggested several proposals to preserve these certificates. However, the challenge is still to have an integrated, tamper-proof and low-cost solution where the certificate issuer and the certificate itself are validated in a single platform. This paper proposes an integrated solution that uses a decentralized blockchain-based certificate verification and issuer validation system. In addition to this, it will protect the certificates from being tampered with. To search faster, hash function mapping has been employed. The proposed solution is experimentally validated by creating a blockchain network using Ethereum where each peer node represents an entity of a certificate verification system such as a validator, certificate issuer, certificate holder and the end-user of the client. The performance of the designed solution is measured by the execution and transaction cost in terms of gas consumption. A comparative analysis has been performed on similar types of tasks reported in the existing work performed on the same platform. It has been observed that the cost incurred for adding a certificate is minimal for the proposed approach. Furthermore, the searching time for the certificates is minimized by using a hash-based searching methodology. The results show that the search time has drastically gone down when certificates are not available.
Madhu S, Sowmya D N, Harish HN, Leelavathy AM, Anupama
Blockchain-based currencies and the decentralized web are reshaping commerce and investment by challenging traditional financial systems and introducing innovative transaction methods. Cryptocurrencies like Bitcoin and Ethereum leverage cryptography to ensure secure, independent transactions without central authorities, while Web 3.0 technologies such as blockchain enable decentralized and trustless systems. These advancements offer faster, more cost-effective, and transparent cross-border transactions by eliminating intermediaries, making them highly efficient for businesses. Additionally, these technologies promote financial inclusion by providing access to financial services for underserved populations, particularly in developing regions. Decentralized finance (DeFi), built on blockchain, has emerged as a transformative force, allowing users to borrow, lend, and trade assets without traditional institutions. The growing adoption of cryptocurrencies and Web 3.0 highlights their potential to revolutionize commerce, enhance efficiency, and create new investment opportunities.
Istiaque Ahmed, Kentaroh Toyoda, Tadashi Nakano, Thi Hong Tran
Traditional digital identity systems struggle with centralization, vulnerability to manipulation, and a lack of transparency. In distributed identity, different cryptographic methods are used for issuing credentials, that create challenges during presentation. It suffer from a fundamental interoperability barrier with heterogeneous digital-signature schemes, forcing each verifier either to implement every scheme or to trust a central translation gateway. We propose a signature-agnostic verification framework that eliminates this barrier. The core idea is to commit a salted root hash of credential claims to a distributed ledger and ensure the authenticity using a smart contract. A zero-knowledge proof (zk-SNARK) is used to prove a selected claim set without revealing actual information. The verification reduces to a single hash-consistency check, and the verifier never touches issuer-specific signatures. A pleasant side effect is that the same verifiable presentation (VP) can be reused across verifiers and sessions, since trust derives from the on-chain anchor rather than transient signatures. This research will advance the identification ecosystem, enabling applications such as eKYC across finance, healthcare, and other sectors. We implement our method on Ethereum Virtual Machine (EVM) using Groth16, benchmark gas cost, proof size, and latency, and show its feasibility and computational efficiency. The privacy and security analysis confirms that the proposed solution is resistant to various attacks.
In order to provide hedging strategies on the financial risks involved in such crises and also taking into consideration that two cryptocurrency prices have been impacted by Russia-Ukraine war uncertainties apart from the COVID-19 pandemic, we applied wavelet analysis along with the multivariate DCC-GARCH process to scrutinize the return–volatility causal relationship among gold price and six stock market indices, including three well-established emerging economy (EE) ones. We achieved a more balanced and complete picture by considering data for the time period July 28, 2016 to December 30, 2022. The events of analysis were crises in the Chinese market, a trade war between the USA and China), caused by the COVID-19 pandemic, after which came global recession Ⅲ (a Russia-Ukraine war); next, part Ⅳ — the peak of the global energy crisis. The findings generally indicated that when a sudden shock sometimes like this happens (or in a pandemic), there is no one other than Ethereum for all investors in emerging and developed markets to find a safe haven or protect themselves, while Bitcoin acts as less safe. We also showed Gold as a hedge in Global Crises and as a Hedge and Weak Safe Haven Against Geopolitical Tension. Last, investors in the paired joint oil stock have a greater benefit but can gain only if they hold shorter-term investments. As for volatility, arguably, only bitcoin is to be observed as the least volatile among all other variables. Our findings suggested that stock markets are the source of volatility spillover to all others while prior work has established mixed evidence during the pandemic, the most crucial and recent periods, respectively.
Naresh Kumar Satish, Mathieu Mercadier, Cristina Hava Muntean, Anderson Augusto Simiscuka
The cryptocurrency market is widely regarded as one of the most volatile financial markets due to inconsistencies in its pricing factors. Despite this volatility, it continues to attract a large population of investors, many of whom incur significant losses. To address this challenge and support risk assessment for investors, users, and other stakeholders, this paper focuses on forecasting Ethereum prices by analyzing social media sentiment. The study gathers data from sources such as global news headlines and Reddit discussion forums, enhancing it with hybrid sentiment features derived from the VADER, BERT and TextBlob models. These sentiment insights are then correlated with Ethereums financial parameters to establish meaningful relationships within the data, which are used to train machine learning models. The study evaluates the predictive performance of Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory models. Among these, Extreme Gradient Boosting demonstrated superior performance, effectively capturing complex relationships within the data and achieving an R-squared value of 0.982115. To further enhance the studys risk assessment capabilities, the concept of Explainable Artificial Intelligence (XAI) is employed to improve transparency and accountability in the model outcomes. Specifically, Shapley Additive Explanations (SHAP) are used to interpret the feature interactions within the Extreme Gradient Boosting model, thereby increasing its reliability and providing deeper insights into its decision-making process.
This study conducts a performance evaluation of a blockchain-based Human Resource Management System (HRMS) utilizing smart contracts to enhance organizational efficiency and scalability. Despite blockchain’s transformative potential through decentralization, transparency, and immutability, empirical research on its scalability for large-scale HRMS applications remains limited. This research addresses this gap by designing, implementing, and testing a blockchain-based HRMS prototype with a simulated dataset of 5000 users across five core HR modules: recruitment, employee management, payroll, leave, and exit/retirement. Leveraging the Ethereum development network, Solidity for smart contract development, and Hyperledger Caliper for performance benchmarking, the study evaluates transaction latency and throughput under escalating transaction loads (5 to 5000 transactions). Results demonstrate exceptional scalability, with consistently low average latency (0.07 - 2.11 seconds) and high throughput (2.4 - 78.1 TPS), affirming the system’s robustness for high-volume HR operations. The findings provide evidence-based insights and recommendations for designing scalable blockchain solutions, contributing to advanced HR practices and organizational performance optimization.
Smart contracts frequently fail due to transaction reverts, yet diagnosing the causes of these failures remains challenging. We present an analysis pipeline that automatically extracts and clusters invariants from on-chain reverted transactions, uncovering the underlying conditions that trigger failures. At the core of our approach is ReBERT, a custom embedding model fine-tuned on invariant data, which outperforms existing semantic similarity models in capturing subtle predicate relationships. Our analysis reveals meaningful clusters of failure causes—such as Access Control, Data Flow, and Status Checks—that highlight recurring vulnerabilities in smart contract execution. These findings advance understanding of failure patterns for Ethereum Smart Contracts.
Mantri Christ Elison, Martin Victor K, Gifton Paul Immanuel
The objective of this research is to develop an R&D (Research and Development) for the hardiness relay alert system, including applying the machine learning, and the fuzzy logic networks for the real time Ethereum transaction 'match failure' detection and the improved Ethereum blockchain security.As an example, the system is computing on the transactions due to the fact the system for transaction analysis corresponds with concrete intrinsic characteristics and thus it mainly takes out suspicious or malicious transactions.The logistic regression, support vector machines (SVM) decision tree and random forests are used in this research and optimized by grid search.Finally, on the other hand, uncertainty problems and false alarms are solved where fuzzy membership functions are used to put transaction attributes into linguistic hobbled variables (such as 'low', 'medium' and 'high').The conclusion of this descriptive research is that fuzzy logic integration with machine learning can improve the approach of anomaly mediation compared to the rules based approach and it is superior to rules based approach.Finally, the effectiveness of the models is detailed and replicated in various graphical representations of the decision making process and membership functions to show that the system can be deployed in real time to secure blockchain networks.
In this study, we examined the regime-dependent dynamics and interrelationships among major cryptocurrencies, Bitcoin (BTC), Ethereum (ETH), and Monero (XMR), using high-frequency one-minute data from January 2020 to April 2025. To capture the presence of latent structural shifts without assuming Markovian transitions, we employed a Gaussian Mixture Model (GMM), which flexibly clustered distributions into two, empirically distinct regimes. Regime-specific Vector Autoregressive (VAR) models were then estimated to analyze interdependencies, spillovers, and shock transmission mechanisms across these digital assets. In the calm regime, the return dynamics were primarily self-driven, with limited cross-asset responses. Conversely, the volatile regime exhibited stronger and more persistent interlinkages, with BTC consistently acting as the principal transmitter of shocks to ETH and XMR, while ETH acts as a secondary transmitter, whereas XMR remains largely a risk recipient, absorbing external shocks with limited feedback into the system. These findings were corroborated through impulse response functions and forecast error variance decompositions, which consistently revealed asymmetric interdependence structures across the regimes. The Granger causality indicated more stable and statistically significant causal relationships in the calm regime than in the volatile regime. Furthermore, the Bai-Perron structural break tests confirmed the absence of significant deterministic breaks in the return series, reinforcing the validity of the GMM-based regime identification. These findings have practical implications for investors, regulators, and risk managers when modeling contagion and developing risk management strategies in cryptocurrency markets, especially during periods of heightened volatility.
The paper presents two series representations of a L{\'e}vy process for the Generalized Tempered Stable (GTS) distribution: a series representation generated by the inverse tail integral and a short noise representation. Both series representations are used to simulate the daily returns of Bitcoin and Ethereum. The Q-Q plot analysis shows smooth linear patterns, indicating strong agreement between the empirical and theoretical GTS distributions.