Forthcoming and Online First Articles

International Journal of Blockchains and Cryptocurrencies

International Journal of Blockchains and Cryptocurrencies (IJBC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Blockchains and Cryptocurrencies (2 papers in press)

Regular Issues

  • A hybrid deep learning-based framework for enhanced real-time fraud detection in Bitcoin transactions   Order a copy of this article
    by Sudip Diyasi, Ankita Ghosh, Dipankar Dey 
    Abstract: Fraud risks are on the rise with the increase in cryptocurrency transactions; the traditional detection methods become inadequate. It proposes a hybrid deep-learning framework for real-time fraud detection in Bitcoin transactions. Algorithms like Random Forest, Support Vector Machine (SVM), Logistic Regression, and XGBoost are used to analyse transaction patterns and anomalies with a high level of accuracy. Different models have been tested in transaction data for Bitcoin, and the best-performing model was XGBoost, with an accuracy of 96.94%. Advanced machine learning techniques enrich a system through secure data-driven insights and real-time anomaly detection, thus enhancing fraud risk detection. The obstacles faced are scalability, privacy issues, and inability to adjust models according to the evolving fraud technique. Future advancements might deal with federated learning, encryption methods, and cross-platform prevention to make the detection of fraud more secure. This paper indicates how well deep learning-based detection of fraud can scale and work efficiently to strengthen trust in digital financial systems.
    Keywords: fraud detection; Bitcoin transactions; hybrid framework; deep learning; cryptocurrency transactions; anomaly detection.
    DOI: 10.1504/IJBC.2025.10070993
     
  • Advancing resilience in Ethereum, Bitcoin, and blockchain technology: a comprehensive analysis of scalability, security, and sustainability challenges in a VUCA world   Order a copy of this article
    by Gabriel Kabanda  
    Abstract: Blockchain technology, exemplified by platforms such as Ethereum and Bitcoin, is reshaping decentralised ecosystems and holds profound implications for socio-economic transformation within an increasingly volatile, uncertain, complex, and ambiguous (VUCA) global environment. Yet, its broader adoption is hindered by challenges related to scalability, security, environmental sustainability, governance, and interoperability. This study, grounded in the Technology-Organisation-Environment (TOE) framework, critically investigates these multifaceted barriers and proposes strategic interventions to optimise blockchain deployment. Employing a mixed-methods approach, the research integrates quantitative performance metrics with qualitative insights from expert interviews and case analyses. It evaluates Ethereum’s transition to proof of stake (PoS), Bitcoin’s evolving economic roles, and the expanding applications of blockchain in DeFi, asset tokenisation, and Central Bank Digital Currencies (CBDCs). Further, it explores governance innovations, privacy safeguards, and the emerging threat of quantum computing. The study presents empirically grounded, theoretically informed recommendations to enhance blockchain’s scalability, security, and compliance, fostering ethical, inclusive, and sustainable digital futures.
    Keywords: blockchain technology; ethereum; bitcoin; resilience; scalability; sustainability; securities; security systems.
    DOI: 10.1504/IJBC.2025.10071745
     

 

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