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A Cybersecurity NER Method Based on Hard and Easy Labeled Training Data Discrimination.

Lin Ye¹, Yue Wu¹, Hongli Zhang¹

¹School of Cyberspace Science, Harbin Institute of Technology, 150001 Harbin, China.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This summary is machine-generated.

This study introduces a cybersecurity Named Entity Recognition (NER) method that discriminates between hard and easy training data. Optimal performance is achieved by balancing these data types, improving NER accuracy in cybersecurity.

Keywords:

cybersecurity NER data augmentation deep learning hard and easy labeled training data pre-trained language models

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Area of Science:

Natural Language Processing
Cybersecurity

Background:

Named Entity Recognition (NER) has advanced in general domains but faces challenges in cybersecurity due to data sensitivity and lack of public datasets.
Existing research often focuses on increasing annotation volume, neglecting the inherent characteristics of cybersecurity training data.

Purpose of the Study:

To propose a novel cybersecurity Named Entity Recognition (NER) method.
To address the limitations of current approaches by discriminating between hard and easy labeled training data.

Main Methods:

A hybrid discriminator combining deep learning (DL) and rule-based approaches was used to partition datasets into hard and easy samples.
The proportion of hard and easy data was optimized for training.
A data augmentation algorithm was applied to the partitioned data.

Main Results:

The ratio of hard to easy samples significantly impacts NER performance.
An optimal 1:1 proportion of hard to easy samples was identified.
The proposed method demonstrated improved overall performance for cybersecurity NER.

Conclusions:

Balancing hard and easy labeled data is crucial for enhancing cybersecurity NER.
The developed method offers a more effective strategy for training NER models in the cybersecurity domain.