NLP4CyberSecurity

中文说明 | English

This code is NLP models and tech implementation for cyber security task, driven by deep learning model, a nice work on cyber security.

本项目使用自然语言处理（NLP）技术应用于网络安全领域，包括恶意软件检测、漏洞发现和威胁情报等方面。该项目基于Python编程语言和机器学习框架Scikit-learn、TensorFlow和Keras等，实现了一些常见的NLP技术，如文本预处理、特征提取、词嵌入、文本分类和主题建模等。通过对网络安全方面的文本数据进行处理和分析，该项目能够提高网络安全人员的工作效率和准确性，以及更好地发现网络安全威胁。此外，该项目还提供了一些用于网络安全的NLP数据集和预训练模型，方便其他研究人员和开发者使用。

Update

NLP4CyberSecurity

Requirement

pip install -r requirements.txt

Usage

Weak Password Detection

weak password detection with machine learning

weak-password/password-strength detection with machine learning; 弱密码检测；密码强度检测

Eval Result


              precision    recall  f1-score   support

           0    0.94406   0.83240   0.88472      8920
           1    0.96327   0.98971   0.97631     49652
           2    0.99035   0.95400   0.97184      8392

    accuracy                        0.96428     66964
   macro avg    0.96589   0.92537   0.94429     66964
weighted avg    0.96410   0.96428   0.96355     66964

XSS Injection Detection

xss injection detection with machine learning

simple nn model

Precision score is : 0.9764296754250387
Recall score is : 0.9830772223302859

simple cnn model

Precision score is : 0.9948463825569871
Recall score is : 0.9762692083252286

simple lstm model

Precision score is : 0.9980311084859225
Recall score is : 0.9869548286604362

Malicious URL Detection

malicious url detection with machine learning

RNN

Accuracy Score is:  0.8655441478439425
Precision Score is : 0.8579050828418984
Recall Score is : 0.8767578205075642
F1 Score:  0.8672290036092299
AUC Score:  0.8655252346603806

CNN

Accuracy Score is:  0.8379671457905544
Precision Score is : 0.8431494883953082
Recall Score is : 0.831085236357673
F1 Score:  0.8370738958974254
AUC Score:  0.8379787529437384

Conv LSTM

Accuracy Score is:  0.9242505133470226
Precision Score is : 0.9288969917958068
Recall Score is : 0.9191095076052642
F1 Score:  0.92397733127254
AUC Score:  0.9242591842604873

Phishing URL Detection

phishing url detection with machine learning

accuracy: 0.9982
Model Accuracy: 99.82%

              precision    recall  f1-score   support

           0    0.99790   0.99895   0.99843      1904
           1    0.99866   0.99732   0.99799      1495

    accuracy                        0.99823      3399
   macro avg    0.99828   0.99814   0.99821      3399
weighted avg    0.99824   0.99823   0.99823      3399

Demo

Samples:

Star-History

Reference

Donation

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cherryL123/NLP4CyberSecurity