This study, "A Comparative Analysis of Ensemble-Based Models for Predicting Cryptocurrency Price Movements," evaluates ensemble machine learning models bagging, boosting, and stacking to improve cryptocurrency price prediction accuracy. Using historical data, models like Random Forest, Gradient Boosting, and Stacking were tested, with Stacking emerging as the top performer (81.80% accuracy, 81.49% F1-score, 88.43% AUC-ROC), outperforming traditional methods like Naive Bayes and Decision Trees. The Boosting Combined model also showed strong results. The research highlights the effectiveness of ensemble techniques in handling cryptocurrency market volatility, offering valuable insights for traders and investors. It underscores the potential of advanced feature engineering and real-time testing to further enhance predictive accuracy, advancing financial decision-making and risk management in the cryptocurrency sector.
Hong-Sheng Huang, Jason Y. Ho, Hao Chen, Hung–Min Sun
Poorly designed smart contracts are particularly vulnerable, as they may allow attackers to exploit weaknesses and steal the virtual currency they manage. In this study, we train a model using unsupervised learning to identify vulnerabilities in the Solidity source code of Ethereum smart contracts. To address the challenges associated with real-world smart contracts, our training data is derived from actual vulnerability samples obtained from datasets such as SmartBugs Curated and the SolidiFI Benchmark. These datasets enable us to develop a robust unsupervised static analysis method for detecting five specific vulnerabilities: Reentrancy, Access Control, Timestamp Dependency, tx.origin, and Unchecked Low-Level Calls. We employ clustering algorithms to identify outliers, which are subsequently classified as vulnerable smart contracts.
Alexander Plotkin, E. Kuznetsov, K. Starodubov, Yuri Gromov
The paper considers the solution of the problem of ensuring the resistance of key infrastructure in distributed registry systems to some destructive influences with the help of multi-factor authentication. Potentially possible destructive influences depending on defects in authentication systems, in particular on network nodes, are presented. Possible methods of providing resistance against such destructive influences are analyzed.
The imminent advent of quantum computing poses a significant threat to the cryptographic foundations of conventional blockchain systems. This research explores the integration of quantum-resistant cryptographic algorithms within decentralized architectures to future-proof blockchain networks. We investigate lattice-based, multivariate, and hash-based signature schemes as viable candidates for post-quantum security and evaluate their performance and scalability within distributed ledger environments. Furthermore, the study proposes a hybrid consensus model combining quantum-secure primitives with classical proof-of-stake mechanisms to enhance resilience and maintain efficiency. Through simulation and empirical validation, the proposed framework demonstrates robustness against quantum-enabled attacks while preserving decentralization, transparency, and immutability. Our findings contribute to the evolution of blockchain technologies towards a secure post-quantum era, establishing a foundation for sustainable and secure decentralized applications across industries.
The paper presents the results of analysing the principles of key infrastructure operation in ‘classical’ systems and their comparison with distributed registry systems, assesses the applicability of key management systems in distributed registries. It is concluded that it is necessary to develop a specialised mathematical model to assess the stability of key infrastructure in distributed registry systems.
Tahrim Hossain, Sheikh Hassan, Faisal Haque Bappy, Muhammad Nur Yanhaona · 6 authors
The emergence of blockchain technology has revolutionized contract execution through the introduction of smart contracts. Ethereum, the leading blockchain platform, leverages smart contracts to power decentralized applications (DApps), enabling transparent and self-executing systems across various domains. While the immutability of smart contracts enhances security and trust, it also poses significant challenges for updates, defect resolution, and adaptation to changing requirements. Existing upgrade mechanisms are complex, resource-intensive, and costly in terms of gas consumption, often compromising security and limiting practical adoption. To address these challenges, we propose FlexiContracts+, a novel scheme that reimagines smart contracts by enabling secure, in-place upgrades on Ethereum while preserving historical data without relying on multiple contracts or extensive pre-deployment planning. FlexiContracts+ enhances security, simplifies development, reduces engineering overhead, and supports adaptable, expandable smart contracts. Comprehensive testing demonstrates that FlexiContracts+ achieves a practical balance between immutability and flexibility, advancing the capabilities of smart contract systems.
In a multi robot system establishing trust amongst untrusted robots from different organisations while preserving a robot's privacy is a challenge. Recently decentralized technologies such as smart contract and blockchain are being explored for applications in robotics. However, the limited transaction processing and high maintenance cost hinder the widespread adoption of such approaches. Moreover, blockchain transactions be they on public or private permissioned blockchain are publically readable which further fails to preserve the confidentiality of the robot's data and privacy of the robot. In this work, we propose RoboComm a Decentralized Identity based approach for privacy-preserving interaction between robots. With DID a component of Self-Sovereign Identity; robots can authenticate each other independently without relying on any third-party service. Verifiable Credentials enable private data associated with a robot to be stored within the robot's hardware, unlike existing blockchain based approaches where the data has to be on the blockchain. We improve throughput by allowing message exchange over state channels. Being a blockchain backed solution RoboComm provides a trustworthy system without relying on a single party. Moreover, we implement our proposed approach to demonstrate the feasibility of our solution.
Blockchain networks provide a reliable trust anchor to decentralized applications (DApps) backed by smart contracts. The Ethereum ecosystem now encompasses most blockchain networks that provide compatible support for smart contracts code. Recently, many Ethereum Layer 2 (L2) rollup solutions emerged, meant to scale the base Layer 1 (L1) network, consequently decreasing transaction fees and diversifying the usage scenarios. Furthermore, the number of blockchain providers that offer access to the network infrastructure for both L1 and L2 continuously increases. A developer is faced with a multitude of deployment options and must weigh between the gains in costs and the losses in trust that are still an issue with L2. A decisive factor in this trade-off can be the use case itself, depending on its security requirements. Still, the evaluation of costs and performance cannot be ignored and should rely on a set of measurable metrics, although choosing the right metrics can be complicated. In this practical experience report, we explore the relevance of several such metrics in choosing between different providers and rollups. For this purpose, we perform evaluations for two use cases of DApps: a voting DApp with high security demands, suited for L1 deployment, and a cost-sensitive supply chain DApp, where L2 can be an option. We analyze a set of basic metrics by comparing these between two highly used access providers, Alchemy and Infura, for the L1 deployment case, and between two of the most popular rollups, Arbitrum One and OP Mainnet (Optimism), for the L2 deployment scenario.
Pieter Hartel, Marianne Junger, Mark van Staalduinen
Abstract Objective This study examines Bitcoin generator (BG) websites on the clear and dark web. It focuses on their prevalence, revenue, and associated warnings, as these sites are suspected scams. Method Data for the study was gathered from the Dark Web Monitor and Iknaio Cryptoasset Analytics. A four-step process was used to identify BG sites and their Bitcoin addresses from 2 million dark websites. Results We found 832 dark web BG sites. The monetary revenue from a dark web BG site is approximately 1/3 smaller per Bitcoin address than from a clear web BG site. There is a concentration of revenue at a few BG sites. Only 24% of Bitcoin addresses on dark web BG sites have ever had money deposited on them. On the dark web, the top three clusters of crypto addresses account for 35% of the total revenue. On the clear web, the top three clusters account for 52% of the total revenue. The longer BG sites are online, the higher the revenue. There are hardly any warnings against BG sites. Conclusion Our results fit the Rational Choice model of crime: the revenue is modest, but the effort of the offenders is also limited.
How do breakthroughs emerge in unpredictable environments? This paper develops a unified framework for systemic innovation by integrating eight key concepts: exaptation, serendipity, emergence, co-optation, bricolage, affordances, recombinant innovation, and effectuation. By synthesizing insights from complexity science, sociology, and entrepreneurship, we reveal how creativity flourishes when innovators repurpose existing elements, harness uncertainty, and leverage unexpected affordances. Unlike conventional models that emphasize structured problem-solving, this framework captures the nonlinear, adaptive, and systemic nature of creativity in innovation ecosystems. We illustrate its applicability across diverse cases—from the exaptation of mRNA vaccines to the recombinant innovation of AI-driven drug discovery, the co-optation of gig work by tech giants, and the emergence of decentralized finance. Our findings suggest that transformative creativity is not a solitary act, but an emergent systems-level process shaped by adaptive recombination and strategic improvisation. By shifting the focus from predictive planning to creative adaptation, this study provides a novel roadmap for navigating uncertainty and fostering systemic change. It offers both scholars and practitioners an actionable lens to harness creativity, unlock latent affordances, and scale innovation in complex environments.
Joy Nnenna Okolo, Adesola Adul-Gafar Arowogbadamu, Samuel Adetayo Adeniji, Rhoda Kalu Tasie
The rapid adoption of mobile AI applications in areas such as healthcare, finance, and personalized services has raised significant concerns about data privacy and security. Traditional centralized machine learning (ML) models require mobile devices to transmit user data to cloud servers, posing risks of data breaches and regulatory non-compliance. Federated learning (FL) addresses these concerns by allowing decentralized AI model training directly on user devices, ensuring that raw data remains private and never leaves the device. However, FL faces security vulnerabilities and performance limitations, including model inversion attacks, data poisoning risks, and high computational overhead. This paper explores key privacy-preserving techniques such as differential privacy, secure aggregation, and homomorphic encryption, which enhance FL security while maintaining model accuracy. Additionally, emerging trends such as blockchain-integrated FL, post-quantum cryptography, and AI-driven optimization are analyzed to highlight the future of privacy-preserving mobile AI ecosystems. By integrating advanced cryptographic techniques and decentralized verification mechanisms, FL can enable scalable, secure, and regulation-compliant AI applications, ensuring a balance between data privacy and AI innovation.
Zero Trust Architecture (ZTA) offers a critical security framework for AI-powered cloud systems, replacing traditional perimeter-based defenses with the principle of "never trust, always verify." As organizations deploy increasingly sophisticated AI workloads in distributed cloud environments, they face unique and acute security challenges including model poisoning, adversarial attacks, and extraction attempts targeting valuable intellectual property. ZTA addresses these challenges through continuous authentication, least privilege access, micro-segmentation, and ongoing monitoring specifically calibrated for AI systems. Implementation requires balancing security with performance considerations, managing complexity, addressing skill gaps, and overcoming technical debt in legacy systems. Emerging approaches including AI-powered security tools, zero-knowledge proofs, hardware-based security measures, and standardized frameworks for autonomous systems are shaping the future of AI security in cloud environments, enabling organizations to realize the benefits of AI innovation while maintaining robust protection.
As a novel decentralized computing paradigm, blockchain is expected to disrupt the existing e-commerce architecture and process. Secure smart contracts are the crucial foundation for e-commerce based on blockchain. However, vulnerabilities in smart contracts occur from time to time and cause significant financial losses in e-commerce. Some static verification methods have been developed to guarantee security for e-commerce smart contracts at design time, but they cannot support complex scenarios at runtime. As a lightweight verification method, runtime verification is a potential method for secure e-commerce smart contracts. The existing runtime verification methods are based on the manual instrument, which leads to additional overheads and gas consumption. To deal with this, we propose a passive learning-based runtime verification framework for e-commerce smart contracts. Firstly, by exploring the Genetic algorithm to evolve state merging and automaton reorganizing in order to simultaneously split time and gas behaviors, we propose a passive learning method to model runtime information for e-commerce smart contracts (PL4ESC). It directly learns P2TA (priced probabilistic timed automaton) from runtime traces without any prior knowledge. Then, we integrate PL4ESC with the open-source PAT (Process Analysis Toolkit) to automatically verify the security of runtime e-commerce smart contracts. The experiments show that PL4ESC is better at accuracy and precision than state-of-the-art passive learning methods. It improves accuracy by 1 to 4 percent compared to TAG and RTI+. As far as we know, it is not only the first learning method that can learn a P2TA from traces, but it is also the first automated runtime verification framework for e-commerce smart contracts. This will provide security guarantees for blockchain-based e-commerce.
Tahrim Hossain, Faisal Haque Bappy, Tarannum Shaila Zaman, Tariqul Islam
This paper introduces CrossLink, a decentralized framework for secure cross-chain smart contract execution that effectively addresses the inherent limitations of contemporary solutions, which primarily focus on asset transfers and rely on potentially vulnerable centralized intermediaries. Recognizing the escalating demand for seamless interoperability among decentralized applications, CrossLink provides a trustless mechanism for smart contracts across disparate blockchain networks to communicate and interact. At its core, CrossLink utilizes a compact chain for selectively storing authorized contract states and employs a secure inter-chain messaging mechanism to ensure atomic execution and data consistency. By implementing a deposit/collateral fee system and efficient state synchronization, CrossLink enhances security and mitigates vulnerabilities, offering a novel approach to seamless, secure, and decentralized cross-chain interoperability. A formal security analysis further validates CrossLink’s robustness against unauthorized modifications and denial-of-service attacks.
Blockchain and smart contracts have emerged as revolutionary technologies transforming distributed computing. While platform evolution and smart contracts' inherent immutability necessitate migrations both across and within chains, migrating the vast amounts of critical data in these contracts while maintaining data integrity and minimizing operational disruption presents a significant challenge. To address these challenges, we present SmartShift, a framework that enables secure and efficient smart contract migrations through intelligent state partitioning and progressive function activation, preserving operational continuity during transitions. Our comprehensive evaluation demonstrates that SmartShift significantly reduces migration downtime while ensuring robust security, establishing a foundation for efficient and secure smart contract migration systems.
The application of Bitcoin enables people to understand blockchain technology gradually. Bitcoin is a decentralized currency that does not rely on third-party credit institutions, and the core of Bitcoin's underlying technology is blockchain. With the increasing value of Bitcoin and the vigorous development of decentralization, people's research on blockchain is also increasing day by day. Today's blockchain technology has not only made great achievements in the application of Bitcoin, but has also been preliminarily applied in other fields, such as finance, medical treatment, the Internet of Things, and so on. However, with the initial application of blockchain technology on the Internet, the security of blockchain technology has also been widely concerned by people in the industry. For example, whether currency trading platforms, smart contracts, blockchain consensus mechanisms, and other technologies are vulnerable to attacks, and how we can defend against these attacks digitally and optimize the blockchain system is exactly the subject we want to study. For the security of appeal blockchain, this paper first analyzes the security threats faced by the application digital currency trading platform of the blockchain system, then analyzes the security problems of smart contract closely related to blockchain 2.0, and then analyzes and studies the security threats of blockchain public chain, consensus mechanism, and P2P. Finally, combined with the security problems at all levels of the blockchain system we analyze and study how to optimize the security of the blockchain system.
With the rapid growth of the Internet of Things (IoT), maintaining data integrity, confidentiality, and authentication is now an imperative challenge. Most conventional cryptographic solutions cannot satisfy the specific constraints of IoT environments, which include limited computational resources, energy efficiency, and scalability. This study proposes a lightweight hybrid cryptographic framework combining Authenticated Encryption with Associated Data (AEAD) and Verifiable Random Functions (VRF) with Elliptic Curve Digital Signature Algorithm (ECDSA). The hybrid framework is intended to offer robust data integrity, secure authentication, and efficient encryption mechanisms with minimal computational overhead. Our solution makes use of AEAD (AES-GCM or ChaCha20-Poly1305) in order to establish both confidentiality and integrity within a single encryption process and with much less processing time than in traditional approaches such as AES-CTR with HMAC. Use of VRF guarantees that cryptographic algorithms result in verifiable randomness that increases replay attack and unauthorized entry security. ECDSA is utilized for lightweight digital signatures, providing non-repudiation without the computational overhead being higher than RSA-based integrity mechanisms. To ensure the efficacy of our methodology, we performed thorough benchmarking tests comparing AEAD + VRF + ECDSA with conventional cryptographic methods like AES-CTR + HMAC and integrity verification based on RSA. It is revealed by our benchmarks that our hybrid solution considerably cuts down encryption time, minimizes CPU utilization, and maximizes memory usage, thus being very suitable for resource-poor IoT devices. In contrast to AES-CTR + HMAC, which needs independent encryption and authentication phases, AEAD's hybrid approach has the least storage footprint and computational overhead. Furthermore, avoiding a dedicated verification step (necessary in HMAC-based designs) adds to system responsiveness. Our work adds to the literature through a scalable, effective, and secure cryptographic framework optimized for IoT use cases such as secure messaging, sensor data encryption, and access control in distributed systems. Real-world deployment in IoT platforms, post-quantum cryptographic augmentation, and implementing zero-knowledge proofs (ZKPs) for improved privacy-preserving authentication are next steps. By solving major problems in IoT security, our hybrid approach provides an efficient yet reliable alternative to state-of-the-art cryptographic solutions to guarantee end-to-end data confidentiality and integrity within contemporary IoT infrastructures.
In this paper Crypto Currency, history and different types are discussed. The main objective of the paper is to do a pilot study on Cryptocurrency with special emphasis on Bit Coin Analysis. The aspects covered in the paper are the history of Crypto currency, and Features and reasons for investment in this currency. A short Analysis is done to compare the performance of Bitcoins between the last quarters of 2023 and 2024. In addition it was shown that the Trade volume and Market Capitalization are high for Bitcoins as compared to other currencies.
The main purpose of this paper is to compare the forecasting results of time series machine learning models to predict the cryptocurrencies’ future prices for pre-COVID-19, during COVID-19 and post COVID 19 pandemics. Time series data collected from Yahoo Finance was used for the period between January 2017 till February 2024 as test data and the training data to predict 12 months from March 2024 till February 2025. The author undertook three machine learning models: SARIMA, LSTM and FbProphet for the forecasting analysis. LSTM model performs well in predicting the daily price forecasting as compared to SARIMA and Fb prophet models. Bitcoin is predicted to be in the range of $55000 to $65000 by February 2025. Results show a robust trend of volatility during COVID and post COVID periods and pre COVID period was not volatile resulting to no price movements. Based on the forecasting results post-COVID-19 pandemic, the LSTM model outperforms with better predictions than the other models. The findings also revealed that LSTM-RNN model can significantly increase the predictive power in the studies of deep learning models. This paper contributes to the literature on machine learning and forecasting models and the finding provides unique information while modeling the returns. It also insights on what machine learning model is the best to predict movements in the time series data. The results in this paper are expected to enhance our understanding on the role of machine learning models in forecasting future prices of investment instruments in the market, making it valuable for academics, investors, and policymakers alike.
The study examines research on Blockchain technology in the global finance using trends analysis, bibliometric analysis, and literature review using the Elsevier Scopus database from 2016-2023. The findings showed that (Blockchain in Financial Management) BCFin research has generated 185 publications comprising 53.5% articles, 45.4% conference proceedings and 1.1% reviews. The subject area analysis revealed three major publications, groupings spanning Computer Science, Engineering, and Decision Sciences. In contrast, the source titles revealed ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences as the top mediums of publications for BCF in researchers. Stakeholders analysis showed that the top researchers and affiliations for BCFin are Jon M. Truby and Qatar University (Qatar), with 4 and % publications, respectively. The most productive nation on the topic is China, which is largely ascribed to active funding agencies such as the National Natural Science Foundation (NNSF) of China, which has funded 18 publications. Keywords Co-occurrence Analysis revealed that the top three keywords on BCFin are Blockchain, Finance, and Supply Chain Finance. Cluster analysis revealed three (3) clusters comprising 4-8 keywords, 132 links, and a TLS of 866. Based on the keywords, these clusters could be broadly categorised as the following terms: Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is an active, multidisciplinary, and impactful research area. BCFin has transformed the global financial industry by overcoming trust issues, enhancing transaction security, and improving communication effectiveness, but it still faces challenges like interoperability, system integration, and sustainability.
Extended reality holds the potential to alter human experiences. This project explores user perceptions of peak experience (PE), a state of self-actualization and fulfillment, within augmented spaces and the potential role it can play for designers and developers to effectively engage immersive World Wide Web (Web3) communities. Semi-structured interviews, including an interactive experience stimulus, with key stakeholders in the Web3 space (staff from global immersive technology entities, brand collaborators, and community members, n = 15) were conducted. The findings reveal rich opportunities for users to experience surprise, delight, and a sense of connection and belonging in these spaces. They further suggest the potential to reshape and extend current user experiences by moving the focus of design toward crafting PEs. This holds great value for users to achieve a fuller sense of self-actualization and for content creators to connect to their communities in more meaningful ways.
Sara A. Safari, Maximilian Janisch, Thomas Lehéricy
In the theory of financial markets, a stylized fact is a qualitative summary of a pattern in financial market data that is observed across multiple assets, asset classes and time horizons. In this article, we test a set of eleven stylized facts for financial market data. Our main contribution is to consider a broad range of geographical regions across Asia, continental Europe, and the US over a time period of 150 years, as well as two of the most traded cryptocurrencies, thus providing insights into the robustness and generalizability of commonly known stylized facts.
Set reconciliation is a fundamental task in distributed systems, particularly in blockchain networks, where it enables synchronization of transaction pools among peers and facilitates block dissemination. Traditional set reconciliation schemes are either statistical, offering success probability as a function of communication overhead and symmetric difference size, or require parametrization and estimation of that size, which can be error-prone. We present CertainSync, a novel reconciliation framework that, to the best of our knowledge, is the first to guarantee successful set reconciliation without any parametrization or estimators. The framework is rateless and adapts to the unknown symmetric difference size. Reconciliation is guaranteed whenever the communication overhead reaches a lower bound derived from the symmetric difference size and universe size. Our framework builds on recent constructions of Invertible Bloom Lookup Tables (IBLTs), ensuring successful element listing as long as the number of elements is bounded. We provide a theoretical analysis proving the certainty of reconciliation for multiple constructions. Our approach is validated by simulations, showing the ability to synchronize sets with efficient communication costs while maintaining guarantees compared to baseline schemes. To further reduce overhead in large universes such as blockchain networks, CertainSync is extended with a universe reduction technique. We compare and validate this extension, UniverseReduceSync, against the basic framework using real Ethereum transaction hash data. Results show a trade-off between lower communication costs and maintaining guarantees, offering a comprehensive solution for diverse reconciliation scenarios.
This paper reviews the emerging literature on blockchain, structuring key insights in a conceptual framework and setting out a research agenda from a socio-technical and information systems (IS) perspective. The review covers 234 blockchain-focused papers across IS, management, computer science and related fields. It categorises blockchain use into three main types and reframes blockchain use within the socio-technical phenomena of which it is a part. This broader framework enables us to aggregate, integrate and synthesise knowledge about blockchain, its values, drivers and barriers, social and technical subsystem(s), as well as potential consequences, both intended and unintended. Using this framework as a basis for analysis, we identify gaps in the existing blockchain literature and outline a research agenda for advancing blockchain studies within the IS field.