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Vulnerability extraction and prediction method based on improved information gain algorithm.

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This study introduces an improved information gain algorithm and deep neural network for computer vulnerability detection, significantly enhancing prediction accuracy and response times for better cybersecurity.

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Area of Science:

  • Computer Security and Cybersecurity
  • Machine Learning Applications in Security
  • Vulnerability Analysis and Prediction

Background:

  • Existing computer security solutions struggle with incomplete vulnerability data, hindering timely response and exploitation chain construction.
  • Difficulty in obtaining pre- and post-permission data for vulnerabilities limits effective security measures.
  • The need for sensitive and rapid solutions for computer vulnerabilities is increasingly critical.

Purpose of the Study:

  • To propose an improved vulnerability extraction and prediction method addressing data incompleteness.
  • To enhance the accuracy and response speed of vulnerability detection using deep neural networks.
  • To validate the method's reliability and effectiveness in real-world security scenarios.

Main Methods:

  • Developed a vulnerability extraction and prediction method utilizing an improved information gain algorithm.
  • Employed a deep neural network as the core framework, incorporating the Dropout method to mitigate overfitting with incomplete data.
  • Validated the method through Mask tests to ensure reliability and validity, assessing false negative and positive rates.

Main Results:

  • Achieved excellent F1 and Recall scores of 0.972 and 0.968, respectively, demonstrating high accuracy.
  • Exhibited a rapid response time of 0.12 seconds, outperforming existing methods in convergence and speed.
  • Demonstrated high prediction accuracy for existing permissions (97.9%) and post-existence permissions (96.8%), adapting to evolving security landscapes.

Conclusions:

  • The proposed method significantly improves vulnerability extraction and prediction accuracy, enabling earlier detection and faster security repair.
  • Enhanced prediction of pre- and post-permissions reduces attack surfaces, minimizes breach risks, and strengthens vulnerability exploitation chain understanding.
  • The model offers broad applicability in public and application security, personal computing, and enterprise cloud environments, bolstering overall network defense capabilities.