🛡️ Anti‑Phishing Email Detector

Analyzes email content, headers, and links to identify phishing attacks, calculate risk scores, store history, and visualize ML evaluation results.

📸 Screenshots

🏷️ Tags

#phishing #email-security #cybersecurity #machine-learning #ai-security #spam-detection #fraud-detection #python #sqlite

👨‍💻 Developed By

Syed Shaheer Hussain © Copyright 2026 – All Rights Reserved

📌 Introduction

Email phishing is one of the most dangerous cyber attacks today. Attackers send fake emails pretending to be banks, companies, or trusted services to:

• Steal passwords
• Hack accounts
• Leak personal data
• Commit financial fraud

This project provides a complete AI‑powered solution to detect such phishing emails before damage happens.

🎯 Mission

✅ Protect users from phishing ✅ Educate users about email threats ✅ Use Machine Learning for smart detection ✅ Provide history, analytics, and transparency ✅ Build a scalable cybersecurity product

🌍 Why This Project Was Made

❓ Problem

• Millions of phishing emails daily
• Humans fail to identify fake emails
• Huge financial & data losses

💡 Solution

• Automated ML‑based phishing detection
• Risk scoring instead of yes/no
• GUI dashboard for non‑technical users

📈 Market Value & Importance

Area	Value
Cybersecurity Market	$300+ Billion
Phishing Attacks	#1 attack vector
AI Security Tools	High demand
Academic Value	FYP / Research
Commercial Value	SaaS / Product

👉 This project can be converted into:

• SaaS product
• Browser extension
• Enterprise email scanner
• API‑based security service

🧠 What is Phishing?

📧 Phishing

A cyber attack where fake emails trick users into revealing:

• Passwords
• OTPs
• Bank details
• Login credentials

🧪 Example

“Your account is suspended. Click here to verify.”

🛡️ What is Anti‑Phishing?

Anti‑phishing systems:

• Analyze email content
• Detect suspicious patterns
• Block or warn users
• Reduce human error

🏗️ Project Architecture

User
 │
 │ Email Input
 ▼
GUI Dashboard (Tkinter)
 │
 ├─ Email Parser
 ├─ Feature Extractor
 ├─ NLP Analyzer
 ├─ ML Classifier
 │
 ▼
Prediction Engine
 │
 ├─ Risk Score
 ├─ Verdict
 │
 ▼
SQLite Database
 │
 ├─ Email History
 └─ Evaluation Data

🔁 Flowchart (Text)

Start
 ↓
Paste Email
 ↓
Parse Headers + Body
 ↓
Extract Features
 ↓
ML Model Prediction
 ↓
Risk Score Calculation
 ↓
Verdict (Safe / Phishing)
 ↓
Save to Database
 ↓
Display Result
 ↓
End

📁 Folder Structure Explained

anti_phishing_email_detector/
│
├── main.py                 → Project entry point
├── gui.py                  → GUI Dashboard
├── classifier.py           → ML prediction logic
├── database.py             → SQLite database
├── email_parser.py         → Email header parsing
├── feature_extractor.py    → Feature extraction
├── nlp_analyzer.py         → NLP analysis
├── utils.py                → Helper utilities
├── requirements.txt        → Dependencies
│
├── data/
│   ├── detector.db         → Email history DB
│   ├── phishing_dataset.csv
│   ├── confusion_matrix.png
│   └── roc_auc_curve.png
│
├── models/
│   └── phishing_model.pkl  → Trained ML model
│
└── ml/
    ├── train_model.py      → Model training
    └── evaluate_model.py   → Model evaluation

⚙️ Technologies Used

🧑‍💻 Programming Languages

• Python 🐍

🤖 Machine Learning

• Scikit‑Learn
• Random Forest Classifier

📊 Data & Storage

• SQLite3
• CSV Dataset

🖥️ GUI

• Tkinter
• Pillow (Images)

📈 Visualization

• Matplotlib
• ROC / AUC
• Confusion Matrix

🧪 Features Implemented

✅ Core Features

1. Email content analysis
2. Header inspection
3. URL & domain checks
4. NLP keyword analysis
5. Risk score generation

✅ ML Features

6. Real trained ML model
7. Model accuracy evaluation
8. Confusion matrix graph
9. ROC / AUC curve

✅ GUI Features

10. Email analyzer dashboard
11. Email history viewer
12. Evaluation plots viewer

🧩 Functions Overview

Function	Purpose
predict_email()	Predict phishing
extract_features()	Feature extraction
save_email_history()	Save results
get_email_history()	View history
train_model.py	Train ML
evaluate_model.py	Evaluate ML

🖥️ GUI Working

🧭 Tabs

1. Analyze Email
2. Email History
3. ML Evaluation

📌 How GUI Works

• Paste email
• Click Analyze
• Get verdict + score
• Saved automatically
• View history anytime
• View ML performance charts

🛠️ Installation Guide (Step‑By‑Step)

🔹 Step 1: Install Python

• Python 3.10+
• Add to PATH

🔹 Step 2: Install Requirements

pip install -r requirements.txt

🔹 Step 3: Dataset

• Place phishing_dataset.csv in data/

▶️ How to Run (Step‑By‑Step)

1️⃣ Train Model

python ml/train_model.py

2️⃣ Evaluate Model

python ml/evaluate_model.py

3️⃣ Run Application

python main.py

🌐 Chrome / Web Hosting?

⚠️ This is a Desktop Application, not web‑hosted.

No:

• Host
• Username
• Password
• Browser login

👉 Future enhancement can convert it into:

• Flask / Django Web App
• Cloud SaaS
• Chrome Extension

🧪 How to Use

1. Open app
2. Paste email
3. Click Analyze
4. Read verdict
5. Check history
6. View evaluation

⚠️ Cautions

Caution

• Model accuracy depends on dataset
• Not 100% guaranteed
• Should be combined with awareness
• Dataset bias possible

📚 What You Learn From This Project

🎓 Technical

• Machine Learning
• Feature engineering
• NLP basics
• GUI development
• SQLite database
• Model evaluation

🧠 Concepts

• Cybersecurity
• Phishing techniques
• AI security systems
• Risk‑based detection

🔮 Future Enhancements

🚀 Planned:

• Real‑time email scanning
• Browser extension
• Deep learning (LSTM)
• Online dashboard
• Feedback learning
• API service
• Cloud deployment

🧾 Disclaimer

Warning

This project is developed for educational and research purposes only. The developer is not responsible for misuse or damages caused by reliance solely on this tool.

📌 Important Notes

Important

• Always verify suspicious emails manually
• Never click unknown links
• Enable 2FA
• Use password managers

🛡️ How to Stay Safe from Phishing

✅ Check sender email ✅ Avoid urgent language ✅ Don’t click random links ✅ Verify before login ✅ Use security tools

💎 Final Value Statement

Note

This project demonstrates a complete real‑world AI cybersecurity solution combining Machine Learning, GUI, databases, and visualization — suitable for academic, professional, and commercial use.

⭐ Support & Engagement

If you find this repository useful or insightful, please consider:

• ⭐ Starring the repository
• 🔁 Sharing it within your network
• 👤 Following my GitHub profile for future projects and updates

Your support helps drive continued innovation and open-source contributions.

— Syed Shaheer Hussain