Securing Blockchain Systems: A Layer-Oriented Survey of Threats, Vulnerability Taxonomy, and Detection Methods

Mohammad Jaminur Islam, Saminur Islam, Mahmud Hossain, Shahid Noor and S. M. Riazul Islam ()
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Mohammad Jaminur Islam: Department of Computer Science, University of California, Riverside, CA 92521, USA
Saminur Islam: Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
Mahmud Hossain: Department of Private Certificate Authority, Amazon Web Services (AWS), Herndon, VA 20171, USA
Shahid Noor: Department of Computer Science, Northern Kentucky University, Highland Heights, KY 41099, USA
S. M. Riazul Islam: School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3FX, UK

Future Internet, 2025, vol. 17, issue 5, 1-36

Abstract: Blockchain technology is emerging as a pivotal framework to enhance the security of internet-based systems, especially as advancements in machine learning (ML), artificial intelligence (AI), and cyber–physical systems such as smart grids and IoT applications in healthcare continue to accelerate. Although these innovations promise significant improvements, security remains a critical challenge. Blockchain offers a secure foundation for integrating diverse technologies; however, vulnerabilities—including adversarial exploits—can undermine performance and compromise application reliability. To address these risks effectively, it is essential to comprehensively analyze the vulnerability landscape of blockchain systems. This paper contributes in two key ways. First, it presents a unique layer-based framework for analyzing and illustrating security attacks within blockchain architectures. Second, it introduces a novel taxonomy that classifies existing research on blockchain vulnerability detection. Our analysis reveals that while ML and deep learning offer promising approaches for detecting vulnerabilities, their effectiveness often depends on access to extensive and high-quality datasets. Additionally, the layer-based framework demonstrates that vulnerabilities span all layers of a blockchain system, with attacks frequently targeting the consensus process, network integrity, and smart contract code. Overall, this paper provides a comprehensive overview of blockchain security threats and detection methods, emphasizing the need for a multifaceted approach to safeguard these evolving systems.

Keywords: blockchain security; vulnerability detection; machine learning; deep learning; consensus attacks; smart contract security; cyber–physical systems (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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