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Improved self-training-based distant label denoising method for cybersecurity entity extractions.

Ke Zhang¹, Yunpeng Wang², Ou Li¹

¹Nuclear Power Institute of China, Chengdu, China.

|December 17, 2024

Summary

This summary is machine-generated.

This study introduces a novel self-training method to improve cybersecurity named entity recognition (NER) by reducing noise in distantly labeled data, enhancing extraction accuracy for crucial cyber information.

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Area of Science:

Computer Science
Artificial Intelligence
Natural Language Processing

Background:

Named Entity Recognition (NER) is vital for extracting cybersecurity information.
Current methods require extensive manual labeling due to a lack of specific corpora.
This necessitates more efficient data annotation techniques.

Purpose of the Study:

To develop an improved self-training method for cybersecurity entity extraction.
To address the challenges of noisy, distantly labeled data.
To enhance the accuracy and efficiency of extracting cybersecurity entities.

Main Methods:

Creation of two cybersecurity domain dictionaries.
Development of an algorithm combining reverse maximum matching and POS tagging for distant label generation.
Implementation of a high-confidence text selection and a constrained self-training algorithm with a teacher-student model.

Main Results:

High-quality cybersecurity distantly-labeled data was obtained.
The constrained self-training algorithm significantly improved NER model performance.
Achieved a 3.5% F1 score improvement for Vendor and 3.35% for Product classes.

Conclusions:

The proposed method effectively denoises distantly labeled cybersecurity data.
The approach enhances the performance of state-of-the-art NER models.
This contributes to more accurate and efficient cybersecurity information extraction.