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<p>Smart contracts, widely utilized in blockchain applications, are vulnerable to security threats that can lead to severe financial and operational consequences. This study introduces the Hierarchical Contextual Embedding with Hybrid Deep Ensemble Network (HCE-HDEN) framework, designed to enhance the detection of critical vulnerabilities, specifically Integer Overflow, Reentrancy, and Timestamp Dependency. The proposed methodology integrates semantic learning techniques with hierarchical code tree architectures, significantly improving detection accuracy by capturing complex contextual relationships within smart contract code. To evaluate its effectiveness, HCE-HDEN was tested against nine state-of-the-art vulnerability detection tools, including SmartCheck, Manticore, Osiris, Oyente, ES, and Slither. Experimental results demonstrate that HCE-HDEN outperforms existing tools, achieving superior detection rates of 75% for Integer Overflow, 95% for Reentrancy, and 88% for Timestamp Dependency, compared to ES, the closest competitor, which achieved detection rates of 64%, 90%, and 80%, respectively. While ES exhibited the fastest execution time at 0.16 seconds, HCE-HDEN maintained a balance between accuracy and efficiency, processing smart contracts within 0.8 seconds. The study further highlights that the optimization strategies employed in HCE-HDEN, such as parallel-processing neural networks and hierarchical code structures, significantly enhance vulnerability detection while ensuring reasonable processing speed. These findings establish HCE-HDEN as a highly effective framework for smart contract security analysis, offering superior accuracy over existing automated tools. Future research will focus on further optimizing execution efficiency while expanding vulnerability detection capabilities to address a broader range of security threats in blockchain-based applications.</p>

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Hierarchical Contextual Embedding with Hybrid Deep Ensemble for IoT Smart Contract Vulnerability Detection

  • 期刊论文

  • 发表日期：

    2025年3月3日

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  • Rakheeba Taseen,
  • Nimrita Koul

摘要

Smart contracts, widely utilized in blockchain applications, are vulnerable to security threats that can lead to severe financial and operational consequences. This study introduces the Hierarchical Contextual Embedding with Hybrid Deep Ensemble Network (HCE-HDEN) framework, designed to enhance the detection of critical vulnerabilities, specifically Integer Overflow, Reentrancy, and Timestamp Dependency. The proposed methodology integrates semantic learning techniques with hierarchical code tree architectures, significantly improving detection accuracy by capturing complex contextual relationships within smart contract code. To evaluate its effectiveness, HCE-HDEN was tested against nine state-of-the-art vulnerability detection tools, including SmartCheck, Manticore, Osiris, Oyente, ES, and Slither. Experimental results demonstrate that HCE-HDEN outperforms existing tools, achieving superior detection rates of 75% for Integer Overflow, 95% for Reentrancy, and 88% for Timestamp Dependency, compared to ES, the closest competitor, which achieved detection rates of 64%, 90%, and 80%, respectively. While ES exhibited the fastest execution time at 0.16 seconds, HCE-HDEN maintained a balance between accuracy and efficiency, processing smart contracts within 0.8 seconds. The study further highlights that the optimization strategies employed in HCE-HDEN, such as parallel-processing neural networks and hierarchical code structures, significantly enhance vulnerability detection while ensuring reasonable processing speed. These findings establish HCE-HDEN as a highly effective framework for smart contract security analysis, offering superior accuracy over existing automated tools. Future research will focus on further optimizing execution efficiency while expanding vulnerability detection capabilities to address a broader range of security threats in blockchain-based applications.