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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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An Improved Vulnerability Exploitation Prediction Model with Novel Cost Function and Custom Trained Word Vector

Mohammad Shamsul Hoque¹, Norziana Jamil¹, Nowshad Amin²

¹College of Computing & Informatics, Universiti Tenaga Nasional, Kajang 43000, Malaysia.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This study introduces a novel method to predict exploitable software vulnerabilities, overcoming class imbalance and overfitting. The new model achieves high accuracy, improving cybersecurity resource allocation.

Keywords:

cloud security management cost function supervised machine learning, modelling and prediction vulnerability exploitation prediction

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Area of Science:

Cybersecurity
Machine Learning
Software Engineering

Background:

Cyber-attacks exploit software and hardware vulnerabilities, with a significant class imbalance between exploited and non-exploited types.
Existing vulnerability exploitation prediction models struggle with overfitting and class imbalance, limiting their practical reliability.
The National Vulnerability Database (NVD) contains rich data beyond CVSS scores for predicting exploitability.

Purpose of the Study:

To develop a reliable vulnerability exploitation prediction model that accurately identifies the most likely exploitable vulnerabilities.
To address the critical class imbalance issue in vulnerability data, which hinders model performance.
To improve cybersecurity resource allocation by prioritizing the patching of high-risk vulnerabilities.

Main Methods:

Designed a novel cost function to effectively manage class imbalance in vulnerability datasets.
Developed a custom-trained word vector using the large text corpus within the NVD for enhanced feature representation.
Integrated the custom word vector as an embedded layer in neural network models for vulnerability prediction.

Main Results:

Achieved high overall performance metrics: accuracy (0.92), precision (0.89), recall (0.98), F1-Score (0.94), and AUC score (0.97).
Successfully overcame the overfitting problem commonly encountered in previous models dealing with class imbalance.
Demonstrated superior performance compared to existing vulnerability exploitation prediction models.

Conclusions:

The novel cost function and custom word vector approach significantly enhance the accuracy and reliability of vulnerability exploitation prediction.
The developed model offers a practical solution for cybersecurity management, enabling efficient resource allocation for vulnerability mitigation.
This research provides a robust framework for predicting exploitable vulnerabilities, contributing to more secure software and cloud systems.