https://github.com/jackaduma/nlp4cybersecurity
NLP model and tech for cyber security tasks
https://github.com/jackaduma/nlp4cybersecurity
code-injection command-injection cross-site-scripting cross-site-scripting-proof cyber-security cybersecurity deep-learning machine-learning malicious-url-detection network-security nlp nlp-deep-learning nlp-machine-learning password-strength phishing-attacks phishing-detection sql-injection text-classification xss-injection
Last synced: 4 months ago
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NLP model and tech for cyber security tasks
- Host: GitHub
- URL: https://github.com/jackaduma/nlp4cybersecurity
- Owner: jackaduma
- License: mit
- Created: 2022-05-07T05:58:59.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-03-22T12:09:41.000Z (almost 3 years ago)
- Last Synced: 2025-04-05T00:25:22.262Z (11 months ago)
- Topics: code-injection, command-injection, cross-site-scripting, cross-site-scripting-proof, cyber-security, cybersecurity, deep-learning, machine-learning, malicious-url-detection, network-security, nlp, nlp-deep-learning, nlp-machine-learning, password-strength, phishing-attacks, phishing-detection, sql-injection, text-classification, xss-injection
- Language: Jupyter Notebook
- Homepage:
- Size: 89.8 MB
- Stars: 87
- Watchers: 2
- Forks: 27
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# **NLP4CyberSecurity**
[](https://github.com/jackaduma/CycleGAN-VC2)
[](https://paypal.me/jackaduma?locale.x=zh_XC)
[**中文说明**](./README.zh-CN.md) | [**English**](./README.md)
------
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。
- [x] Dataset
- [x] weak password
- [x] xss injection
- [x] malicious url
- [x] phishing url
- [x] Usage
- [x] Training
- [x] Example
- [ ] Demo
- [x] Reference
------
## **Update**
------
## **Table of Contents**
- [**NLP4CyberSecurity**](#nlp4cybersecurity)
- [**Update**](#update)
- [**Table of Contents**](#table-of-contents)
- [**Requirement**](#requirement)
- [**Usage**](#usage)
- [**Weak Password Detection**](#weak-password-detection)
- [**Eval Result**](#eval-result)
- [**XSS Injection Detection**](#xss-injection-detection)
- [**simple nn model**](#simple-nn-model)
- [**simple cnn model**](#simple-cnn-model)
- [**simple lstm model**](#simple-lstm-model)
- [**Malicious URL Detection**](#malicious-url-detection)
- [**RNN**](#rnn)
- [**CNN**](#cnn)
- [**Conv LSTM**](#conv-lstm)
- [**Phishing URL Detection**](#phishing-url-detection)
- [**Demo**](#demo)
- [**Star-History**](#star-history)
- [**Reference**](#reference)
- [**Donation**](#donation)
- [**License**](#license)
------
## **Requirement**
```bash
pip install -r requirements.txt
```
## **Usage**
---
## [**Weak Password Detection**](./01_weak_password_detect.ipynb)
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**](02_xss_injection_detect.ipynb)
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**](03_malicious_url_detect.ipynb)
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**](04_phishing_url_detect.ipynb)
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**
If this project help you reduce time to develop, you can give me a cup of coffee :)
AliPay(支付宝)
WechatPay(微信)
[](https://paypal.me/jackaduma?locale.x=zh_XC)
------
## **License**
[MIT](LICENSE) © Kun