NLP4CyberSecurity

中文说明 | English

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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。

• Dataset
  • weak password
  • xss injection
  • malicious url
  • phishing url
• Usage
  • Training
  • Example
• Demo
• Reference

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Update

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Table of Contents

• NLP4CyberSecurity
  • Update
  • Table of Contents
  • Requirement
  • Usage
  • Weak Password Detection
    • Eval Result
  • XSS Injection Detection
    • simple nn model
    • simple cnn model
    • simple lstm model
  • Malicious URL Detection
    • RNN
    • CNN
    • Conv LSTM
  • Phishing URL Detection
  • Demo
  • Star-History
  • Reference
  • Donation
  • License

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Requirement

pip install -r requirements.txt

Usage

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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

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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

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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

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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

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Demo

Samples:

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Star-History

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Reference

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Donation

If this project help you reduce time to develop, you can give me a cup of coffee :)

AliPay(支付宝)

WechatPay(微信)

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License

MIT © Kun