Citation

Khan, Fazeel Ahmed and Abdul Kadir, Andi Fitriah and Ibrahim, Adamu Abubakar and Khan, Mohammad Shadab (2026) A critical review on the convergence of blockchain and machine learning in deep packet inspection systems to enhance network traffic security, performance and management. Physica Scripta, 101 (35). p. 355004. ISSN 0031-8949

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Abstract

IOP Science home Accessibility Help Journals Books Publishing Support Login Physica Scripta Inclusive Publishing Trusted Science, find out more. Purpose-Led Publishing, find out more. Paper A critical review on the convergence of blockchain and machine learning in deep packet inspection systems to enhance network traffic security, performance and management Fazeel Ahmed Khan*, Andi Fitriah Binti Abdul Kadir, Adamu Abubakar Ibrahim and Mohammad Shadab Khan Published 28 August 2026 • © 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. Physica Scripta, Volume 101, Number 35 Citation Fazeel Ahmed Khan et al 2026 Phys. Scr. 101 355004 DOI 10.1088/1402-4896/ae999c Authors References Open science Article metrics 34 Total downloads Submit Submit to this Journal Permissions Get permission to re-use this article Share this article Article information Abstract The growing volume and complexity of network data requires advance solutions for network traffic analysis and security. The deep packet inspection (DPI) offers a granular approach to monitor, filter and classify network traffic to enforce security policies, optimize quality of service (QoS) and detect malicious activities. This survey has addressed these issues by exploring the emerging but promising integration of blockchain and machine learning to improve DPI to secure networks and increase performance efficiency. It provides comprehensive details on the application domain of DPI with a focus on network security, performance and management. Also, the survey proposed a research roadmap to guide the future development on blockchain-enabled intelligent solutions for DPI. Using PRISMA methodology, several existing studies were evaluated which addresses the potential application of blockchain and machine learning in DPI. The survey has identified significant challenges towards the integration including real-time IP packet inspection efficiency, QoS performance and the impact of high traffic volume on DPI. It concludes that DPI has wider applications to be integrated with emerging technologies particularly in machine learning and blockchain. The future research should focus on advance machine learning paradigms such as continual and federated learning while blockchain technology should be resolved with scalability challenges to be utilized effectively for next-generation DPI solutions.

Item Type:	Article
Uncontrolled Keywords:	Blockchain and machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	01 Oct 2026 01:29
Last Modified:	01 Oct 2026 01:29
URII:	http://shdl.mmu.edu.my/id/eprint/16743

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