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Recurrent quantum embedding neural network and its application in vulnerability detection.

Zhihui Song1, Xin Zhou1, Jinchen Xu1,2

  • 1Information Engineering University, Zhengzhou, 450001, China.

Scientific Reports
|June 13, 2024
PubMed
Summary
This summary is machine-generated.

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This study introduces a Recurrent Quantum Embedding Neural Network (RQENN) for efficient software vulnerability detection. The novel approach significantly reduces computational resources and enhances accuracy compared to existing methods.

Area of Science:

  • Computer Science
  • Quantum Computing
  • Artificial Intelligence

Background:

  • Deep learning and Natural Language Processing (NLP) show promise in software vulnerability detection.
  • High computational resource demands of NLP hinder scalability.
  • Quantum computing offers potential solutions for resource-intensive AI tasks.

Purpose of the Study:

  • To develop a novel Recurrent Quantum Embedding Neural Network (RQENN) for vulnerability detection.
  • To decrease memory consumption in classical vulnerability detection models.
  • To improve the performance of quantum natural language processing (QNLP) methods.

Main Methods:

  • Implementation of a Recurrent Quantum Embedding Neural Network (RQENN).
  • Application of QNLP techniques for code analysis.

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  • Comparative analysis against classical models and other QNLP methods.

    • Main Results:

    • RQENN exponentially reduces space complexity and significantly lowers parameter and bit consumption compared to classical models.
    • RQENN achieves a 15.7% higher accuracy in vulnerability detection than other QNLP methods.
    • RQENN utilizes fewer qubit resources.

    Conclusions:

    • RQENN offers a computationally efficient and highly accurate solution for software vulnerability detection.
    • The proposed model advances the field of QNLP for security applications.
    • RQENN demonstrates the potential of quantum computing to address limitations in classical AI for cybersecurity.