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Updated: Sep 6, 2025

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SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection.

Lejun Zhang1,2,3, Yuan Li1, Tianxing Jin4

  • 1College of Information Engineering, Yangzhou University, Yangzhou 225127, China.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
Summary

This study introduces a novel hybrid model, the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC), for enhanced smart contract vulnerability detection in the Internet of Things (IoT). The model significantly improves the security of blockchain-based IoT systems.

Keywords:
IoTblockchaindeep learningserial hybrid networksmart contractvulnerability detection

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Blockchain Technology

Background:

  • The proliferation of Internet of Things (IoT) devices necessitates robust trust mechanisms due to their deep integration into personal privacy.
  • Security vulnerabilities in IoT devices and their associated smart contracts pose significant risks that cannot be overlooked.
  • Blockchain technology offers potential solutions for IoT security, highlighting the critical need for secure smart contracts.

Purpose of the Study:

  • To propose and evaluate a novel hybrid model for detecting vulnerabilities in smart contracts.
  • To enhance the security and trustworthiness of smart contract applications within the Internet of Things ecosystem.
  • To address the limitations of existing methods in identifying complex smart contract vulnerabilities.

Main Methods:

  • Development of a flexible and systematic hybrid model named Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC).
  • Introduction of a novel serial-parallel convolution (SPCNN) technique for feature extraction, preserving temporal structure and location information.
  • Utilization of an Ensemble Classifier to improve the robustness of the vulnerability detection process.
  • Creation of two datasets, CESC and UCESC, for multi-task vulnerability detection experiments focusing on six typical smart contract vulnerabilities.

Main Results:

  • The proposed SPCBIG-EC model demonstrated superior performance in smart contract vulnerability detection compared to existing methods.
  • SPCBIG-EC achieved high F1-scores: 96.74% for reentrancy, 91.62% for timestamp dependency, and 95.00% for infinite loop vulnerabilities.
  • The SPCNN component effectively extracted relevant features for multivariate combinations while maintaining sequence integrity.

Conclusions:

  • The SPCBIG-EC model represents a significant advancement in smart contract security, offering a robust solution for vulnerability detection.
  • The hybrid approach effectively addresses the complexities of identifying diverse smart contract vulnerabilities.
  • The findings underscore the importance of advanced deep learning models for securing blockchain-based IoT systems.