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Related Experiment Videos

Multimodal hybrid recurrent framework with selective subpattern activation for smart contract vulnerability

Nivedhitha Gopal1, Radha Senthilkumar2, Mehal Sakthi Muthusamy Sivaraja3

  • 1Department of Information Technology, Madras Institute of Technology (Anna University), Chennai, 600044, India. nivedhithagopal25@gmail.com.

Scientific Reports
|April 18, 2026
PubMed
Summary

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This study introduces a new framework for detecting smart contract vulnerabilities, improving blockchain security. The multimodal hybrid recurrent model enhances accuracy and provides interpretable insights into code flaws.

Area of Science:

  • Blockchain Security
  • Software Engineering
  • Artificial Intelligence

Background:

  • Smart contract vulnerabilities pose significant financial risks in decentralized systems.
  • Accurate and interpretable detection of flaws like reentrancy and timestamp dependence is challenging due to complex code semantics.

Purpose of the Study:

  • To propose a multimodal hybrid recurrent framework for enhanced smart contract vulnerability detection.
  • To introduce a Selective Subpattern Activation (SSA) mechanism for improved interpretability and feature extraction.

Main Methods:

  • Integrating sequential and structural code representations using Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM).
  • Employing the Selective Subpattern Activation (SSA) mechanism to highlight vulnerability-indicative subpatterns.
Keywords:
BiGRUBiLSTMDeep learningFeature fusionSmart contract securityVulnerability detection

Related Experiment Videos

  • Evaluating the framework on a public Ethereum smart contract dataset.

    • Main Results:

    • Achieved 92.16% accuracy and 88.83% F1 score for reentrancy vulnerability detection.
    • Outperformed baseline deep learning and graph-based models in vulnerability detection.
    • Ablation studies confirmed the SSA mechanism's positive impact on performance and interpretability.

    Conclusions:

    • The proposed multimodal framework offers a robust and interpretable solution for smart contract vulnerability detection.
    • The SSA mechanism is crucial for enhancing both detection accuracy and model explainability.
    • This approach contributes to securing decentralized systems against critical code flaws.