CyBERT-CyNER Open-Source Cybersecurity Entity Recognition Model - Accurately Identify Named Entities in Cyber Threats

Cybert CyNER

Developed by Cyber-ThreaD

A cybersecurity entity recognition model fine-tuned on the CyNER dataset based on CYBERT, used to identify named entities related to cyber threats.

Sequence Labeling

Transformers

#Cybersecurity Entity Recognition #Threat Intelligence Analysis #High-precision NER

Downloads 31

Release Time : 12/6/2023

Model Overview

This model is specifically designed for the cybersecurity field as a Named Entity Recognition (NER) model, capable of identifying threat-related entities in text, such as attackers, vulnerabilities, malware, etc.

Model Features

Cybersecurity Specialization

Entity recognition capabilities optimized specifically for cybersecurity domain texts

High Accuracy

Achieves 95.68% accuracy on the evaluation set

Fine-grained Entity Recognition

Capable of identifying multiple types of cybersecurity-related entities

Model Capabilities

Cybersecurity Text Analysis

Threat Intelligence Extraction

Security Incident Report Processing

Use Cases

Security Operations

Threat Intelligence Analysis

Automatically extracts key threat entities from security reports

Improves analysts' efficiency in processing threat intelligence

Security Incident Report Processing

Automatically identifies key elements in security incident reports

Accelerates incident response processes

Security Research

Vulnerability Research

Extracts vulnerability-related information from technical documents

Assists researchers in quickly locating key information

🚀 Cyber-ThreaD/CyBERT-CyNER

This model is a fine - tuned version of SynamicTechnologies/CYBERT on the CyNER dataset, achieving high performance in evaluation and prediction.

🚀 Quick Start

This model is a fine - tuned version of SynamicTechnologies/CYBERT on the CyNER dataset. It achieves the following results on the evaluation set:

Loss: 0.2405
Precision: 0.4671
Recall: 0.2810
F1: 0.3509
Accuracy: 0.9568

It achieves the following results on the prediction set:

Loss: 0.2747
Precision: 0.5442
Recall: 0.3483
F1: 0.4248
Accuracy: 0.9471

✨ Features

Base Model: SynamicTechnologies/CYBERT
Tags: generated_from_trainer
Metrics: precision, recall, f1, accuracy

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2304	1.42	500	0.2405	0.4671	0.2810	0.3509	0.9568
0.1092	2.84	1000	0.2575	0.5426	0.2848	0.3735	0.9601
0.0797	4.26	1500	0.2454	0.4701	0.3308	0.3883	0.9576
0.0615	5.68	2000	0.2669	0.4902	0.3180	0.3857	0.9586
0.0504	7.1	2500	0.2687	0.4885	0.3525	0.4095	0.9580
0.0379	8.52	3000	0.2752	0.4656	0.3627	0.4078	0.9573
0.0339	9.94	3500	0.2828	0.4991	0.3499	0.4114	0.9586

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Citing & Authors

If you use the model kindly cite the following work

@inproceedings{deka2024attacker,
  title={AttackER: Towards Enhancing Cyber - Attack Attribution with a Named Entity Recognition Dataset},
  author={Deka, Pritam and Rajapaksha, Sampath and Rani, Ruby and Almutairi, Amirah and Karafili, Erisa},
  booktitle={International Conference on Web Information Systems Engineering},
  pages={255--270},
  year={2024},
  organization={Springer}
}

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご