https://github.com/saadasif-nu/deepphish
DeepPhish is a web-based phishing detection system that helps users identify suspicious URLs and emails. It uses Machine Learning and Natural Language Processing (NLP) techniques.
https://github.com/saadasif-nu/deepphish
expressjs flask lemmatization machine-learning-algorithms mongodb multinomial-naive-bayes natural-language-processing nodejs python3 reactjs tf-idf vectorization xgboost
Last synced: 3 months ago
JSON representation
DeepPhish is a web-based phishing detection system that helps users identify suspicious URLs and emails. It uses Machine Learning and Natural Language Processing (NLP) techniques.
- Host: GitHub
- URL: https://github.com/saadasif-nu/deepphish
- Owner: SaadAsif-NU
- Created: 2025-06-18T19:12:04.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-06-22T18:27:01.000Z (7 months ago)
- Last Synced: 2025-08-04T12:34:25.177Z (6 months ago)
- Topics: expressjs, flask, lemmatization, machine-learning-algorithms, mongodb, multinomial-naive-bayes, natural-language-processing, nodejs, python3, reactjs, tf-idf, vectorization, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 918 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🔐 DeepPhish — AI-Powered Phishing Detection System
DeepPhish is a web-based phishing detection system that helps users identify suspicious URLs and emails. It uses Machine Learning and Natural Language Processing (NLP) techniques to flag phishing threats and offers a simple, secure interface with user login and history tracking.
---
## 🚀 Features
- 🔗 **URL Phishing Detection** — Analyze and classify URLs as safe or phishing.
- 📧 **Email Body Analysis** — Check raw email text to detect phishing patterns.
- 👤 **User Authentication** — Register and log in to track your own detection history.
- 🕓 **Scan History** — Review previous URL and email scans with results.
---
## 🧰 Tech Stack
### 💻 Frontend
- React.js
- HTML5 / CSS3
### 🖥 Backend
- Node.js (Express)
- Python (Flask)
### 🤖 Machine Learning
- XGBoost (URL Detection)
- Multinomial Naive Bayes (Email Text Detection)
- CountVectorizer for NLP processing
### 🛢 Database
- MongoDB (Mongoose)
---
## 📂 Project Structure
```
DeepPhish -- Phishing Detection Application/
├── Backend/ # Node.js API for user management and detection logic
├── Backend/data # Pre-labeled phishing and safe URLs
├── Python/DeepPhish--Python # Flask server with ML models
├── React/front-end/ # React app for UI
````
---
### ⚙️ Setup Instructions
### GO into directory DeepPhish -- Phishing Detection Application
### 1. Start Backend (Node.js)
Make sure your MongoDB is running locally.
```bash
cd Backend
npm install #for first time
node app.js
```
Make sure `.env` file is configured correctly:
```env
PORT=####
MONGO_URI=mongodb+srv://:@cluster.mongodb.net/DeepPhish
```
for example i used it as
PORT=5001
MONGO_URI=mongodb://127.0.0.1:27017/phishingdb
### 2. Start Python ML Server (Flask)
```bash
cd ../Python
cd DeepPhish--Python
pip install -r requirements.txt
python app.py
```
### 3. Start Frontend (React)
```bash
cd ../React/front-end
npm install # only if node_modules was deleted
npm start
```
---
## 🔒 Signup / Login Info
### 🔐 Password Format
* Minimum 8 characters
* Must include one number and one special character
* Example: `Secure@123`
### ✅ Email Format
* Must be in standard format (e.g., `user@example.com`)
---
## 🧪 How to Use
1. **Signup/Login** to your account.
2. Navigate to:
* **Check URL** — Enter a website link.
* **Check Email** — Paste the email content/body.
3. The system will return:
* `Phishing` or `Safe`
4. Go to **View History** to see all previously scanned results.
---
## 🧭 Future Scope
* SMS Phishing Detection
* Admin dashboard
* Chrome extension
* Real-time alert system
---
## 🧑💻 Author
**Saad Asif**
Final Year CS Student | Cybersecurity, AL, ML & Full Stack Enthusiast
LinkedIn: [https://www.linkedin.com/in/saadasif-nu/](https://www.linkedin.com/in/saadasif-nu/)
**Muhammad Hammad**
Final Year CS Student | Cybersecurity, AL, ML & Full Stack Enthusiast
LinkedIn: [https://www.linkedin.com/in/muhammad-hammad-69aa36248/](https://www.linkedin.com/in/muhammad-hammad-69aa36248/)