Securing Blockchain Systems: A Layer-Oriented Survey of Threats, Vulnerability Taxonomy, and Detection Methods DOI Creative Commons
Mohammad Jaminur Islam, Saminur Islam, Mahmud Hossain

et al.

Future Internet, Journal Year: 2025, Volume and Issue: 17(5), P. 205 - 205

Published: May 3, 2025

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

Language: Английский

Securing Blockchain Systems: A Layer-Oriented Survey of Threats, Vulnerability Taxonomy, and Detection Methods DOI Creative Commons
Mohammad Jaminur Islam, Saminur Islam, Mahmud Hossain

et al.

Future Internet, Journal Year: 2025, Volume and Issue: 17(5), P. 205 - 205

Published: May 3, 2025

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

Language: Английский

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