Journal of Cyber Security and Risk Auditing

Journal of Cyber Security and Risk Auditing

ISSN: 3079-5354 (Online)

Publishing model:

: Open access
open accessOpen Access

Article

👁️1views

A Framework for Transparent and Secure Digital Trading Using Decentralized Applications

by 

Alwi M Bamhdi Orcid link

PDF logoPDF

Published: 2026/03/31

Abstract

The recent digital trading platforms have raised apprehensions about transparency, safety, scalability, and trust, especially for Centrally Managed Systems (CMS), which have vulnerabilities such as single points of failure, data manipulation, and cyberattacks. The purpose of this research is to design and implement a multi-tiered digital trading system using blockchain and decentralized applications (DApps) to improve transactional safety, transparency, and operational effectiveness. The proposed system combines the use of cryptographic hashing, digital signatures, and dual consensus mechanisms (PoW, PoS) to guarantee transaction validation, decentralized democratic governance, and fault tolerance. Considerable empirical investigations were undertaken to assess the suggested multi-tiered system alongside its competitors in the domains of traditional centralized systems, blockchain systems, and distributed ledger technologies. It is shown to provide an estimated throughput of 1200 transactions per second (TPS) compared to other blockchain competitors (TPS 950) and centralized competitors (TPS 700). It boasts a Latency of 25 ms, a 64% improvement compared to other traditional competitors. The new system reached a security score of 98%, transparency of 95%, and privacy of 99%. The system surpassed the other systems and attained unprecedented results. The Scalability Analysis exceeded expectations and reached a score of 95, a prominent indicator of the system’s ability to withstand very high transactional pressures. Governance metrics display enduring exemplary balance and reliable distributed governance, characterized by outstanding audit (9/10), trace (9/10), transparency (10/10), and verification (9/10) metrics. The system also has impressive high fault tolerance (99%), consensus efficiency (97%) and even more delegated low energy consumption. Overall, the proposed system unchained the trading businesses as the outstanding potential in seamless trading transactions. The results also confirm the framework’s effectiveness as a flexible and low-cost substitute for other trading systems, which positions it perfectly to cater to the fast-changing Decentralized Digital Trading Ecosystems.

Keywords

Consensus HybridizationDecentralized GovernanceFault-Tolerant TradingMulti-Tier Blockchain ArchitectureScalable Digital CommerceTransparent Transaction Validation.

A Framework for Transparent and Secure Digital Trading Using Decentralized Applications is licensed under CC BY 4.0CCBY

References

  1. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Retrieved December 21, 2022, from https://bitcoin.org/bitcoin.pdf
  2. Bayer, D., Haber, S., & Stornetta, W. S. (1993). Improving the efficiency and reliability of digital time-stamping. In Sequences II (pp. 329–334). Springer.
  3. Monrat, A. A., Schelen, O., & Andersson, K. (2019). A survey of blockchain from the perspectives of applications, challenges, and opportunities. IEEE Access, 7, 117134–117151.
  4. Nair, R., Zafrullah, S. N., Vinayasree, P., Singh, P., Zahra, M. M. A., Sharma, T., & Ahmadi, F. (2022). Blockchain-based decentralized cloud solutions for data transfer. Computational Intelligence and Neuroscience, 2022, Article 8209854. https://doi.org/10.1155/2022/8209854
  5. Maesa, D., & Mori, P. (2020). Blockchain 3.0 applications survey. Journal of Parallel and Distributed Computing, 138, 99–114.
  6. Alammary, A., Alhazmi, S., Almasri, M., & Gillani, S. (2019). Blockchain-based applications in education: A systematic review. Applied Sciences, 9(2400).
  7. Nair, R., & Bhagat, A. (2020). Healthcare information exchange through blockchain-based approaches. In Transforming businesses with bitcoin mining and blockchain applications (pp. 234–246). IGI Global. https://doi.org/10.4018/978-1-7998-0186-3.ch014
  8. Agbo, C. C., Mahmoud, Q. H., & Eklund, J. M. (2019). Blockchain technology in healthcare: A systematic review. Healthcare, 7(56).
  9. Andoni, M., et al. (2019). Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and Sustainable Energy Reviews, 100, 143–174.
  10. Reyna, A., Martín, C., Chen, J., Soler, E., & Díaz, M. (2018). On blockchain and its integration with IoT: Challenges and opportunities. Future Generation Computer Systems, 88, 173–190.
  11. Alabdulwahhab, F. A. (2018). Web 3.0: The decentralized web blockchain networks and protocol innovation. In Proceedings of the 1st International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1–4).
  12. Xu, M., Chen, X., & Kou, G. (2019). A systematic review of blockchain. Financial Innovation, 5, 1–14.
  13. Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on blockchain technology?—A systematic review. PLoS ONE, 11, e0163477.
  14. Paulavičius, R., Grigaitis, S., Igumenov, A., & Filatovas, E. (2019). A decade of blockchain: Review of the current status, challenges, and future directions. Informatica, 30, 729–748.
  15. Zhou, N., Wu, M., Wang, R., & Wang, D. (2023). Research on the architecture of transactional smart contracts in blockchain systems. Electronics, 12(18), 3923. https://doi.org/10.3390/electronics12183923
  16. Suresh, V., & Palanisamy, K. (2024). Blockchain-aware decentralized identity management and access control (BADIMAC). Computer Networks, 246, 110567. https://doi.org/10.1016/j.comnet.2024.110567
  17. Sun, M., Bi, Y., & Xie, L. (2024). A blockchain and zero-knowledge proof based data security scheme for data trading. Electronics, 13(21), 4260. https://doi.org/10.3390/electronics13214260
  18. Zheng, Z., Zhang, S., Huang, Z., & Gong, T. (2020). An overview on smart contracts: Challenges, advances and platforms. Future Generation Computer Systems, 105, 475–491.
  19. Methmal, J. W. (2023). Zero-knowledge proofs: A comprehensive review of applications, protocols, and future directions in cybersecurity (Technical report). Asia Pacific Institute of Information Technology.
  20. Aad, A., Goldwasser, I., & Micali, S. (2023). Zero-knowledge proof. In Trends in data protection and encryption technologies. Springer.
  21. Othman, U., & Callahan, J. (2017). The Horcrux protocol: A method for decentralized biometric-based self-sovereign identity. arXiv. https://doi.org/10.48550/arXiv.1711.07127
  22. Bartoletti, D., & Pompianu, L. (2017). An empirical analysis of smart contracts: Platforms, applications, and design patterns. arXiv. https://doi.org/10.48550/arXiv.1703.06322
  23. Chaffer, T. E., & Goldston, J. (2022). On the existential basis of self-sovereign identity and soulbound tokens: An examination of the “self” in the age of Web3. Frontiers in Blockchain.
  24. Almomani, O., Arabiat, A. M., Al-Ahmed, H., & Alsariera, E. (2026). Hybridization of deep learning models for multiclass attack detection in wireless sensor networks. Journal of Communications, 21(1), 20–34.
  25. Almomani, O., Arabiat, A., Al Tayeb, M., Almaiah, M. A., Obeidat, M., Aldhyani, T. H., Shehab, R., & Rowad, M. (2025). A robust model for Android malware detection via ML and DL classifiers. Mesopotamian Journal of Big Data, 261–277. https://doi.org/10.58496/MJBD/2025/017
  26. Shambour, Q., Al-Zyoud, M., & Almomani, O. (2025). Quantum-inspired hybrid metaheuristic feature selection with SHAP for optimized and explainable spam detection. Symmetry, 17(10), 1716. https://doi.org/10.3390/sym17101716
  27. Alsaaidah, D., Almomani, O., Abu-Shareha, A. A., Abualhaj, M. M., & Achuthan, A. (2024). ARP spoofing attack detection model in IoT network using machine learning: Complexity vs. accuracy. Journal of Applied Data Sciences, 5(4), 1850–1860.
  28. Weilenmann, T. M., Schneider, M., & Bernal-Manzanedo, A. (2020). A comprehensive survey on smart contract construction and execution: Paradigms, tools, and systems. arXiv. https://doi.org/10.48550/arXiv.2008.13413
  29. Ang, S., Ho, M., Huy, S., & Janarthanan, M. (2026). A multi-layered adaptive cybersecurity framework for the banking sector integrating next-gen firewalls with AI-driven IDPS. STAP Journal of Security Risk Management, 2026(1), 67–76.
  30. Huy, S., Ang, S., Ho, M., & Balasubramaniam, V. (2026). Securing API ecosystems in banking: A critical review of cyber risks, control frameworks, and future trends. Jordanian Journal of Informatics and Computing, 2026(1), 25–37.
  31. Kadhim, A. F., Hamzah, A. E., Al-Shareeda, M. A., Hussein, A. I., & Sapiee, N. M. (2026). Accurate network intrusion detection using a feedforward neural network and bee colony optimization algorithm. International Journal of Cybersecurity Engineering and Innovation, 2026(1).
  32. Al-Na’amneh, Q., Aljawarneh, M., Alhazaimeh, A. S., Hazaymih, R., & Shah, S. M. (2025). Securing trust: Rule-based defense against on/off and collusion attacks in cloud environments. STAP Journal of Security Risk Management, 2025(1), 85–114.
  33. Ho, M., Ang, S., Huy, S., & Janarthanan, M. (2026). MUMSPI: A model for usability measurement of single-platform interface for multi-tasking in big data tools. Jordanian Journal of Informatics and Computing, 2026(1), 1–14.
  34. Ibrahim, A., Kadhim, A. F., Hamzah, A. E., & Al-Shareeda, M. A. (2026). A secure and scalable IoT home automation architecture with web and biometric control. International Journal of Cybersecurity Engineering and Innovation, 2026(1).
  35. Ho, M., Ang, S., Huy, S., & Janarthanan, M. (2026). Cybersecurity risks and challenges in smart cities: A review with insights for Cambodia. STAP Journal of Security Risk Management, 2026(1), 87–97.
  36. Addula, S. R., Norozpour, S., & Amin, M. (2025). Risk assessment for identifying threats, vulnerabilities and countermeasures in cloud computing. Jordanian Journal of Informatics and Computing, 2025(1), 38–48.
  37. Yassin, A., & Almaiah, M. (2026). Cyber security risk assessment for determining threats and countermeasures for banking systems. International Journal of Cybersecurity Engineering and Innovation, 2026(1).