File size: 2,977 Bytes
26b0571
 
369574e
26b0571
369574e
 
 
26b0571
 
369574e
df7e62a
369574e
df7e62a
369574e
df7e62a
369574e
 
 
 
 
 
df7e62a
369574e
df7e62a
369574e
 
 
 
 
df7e62a
369574e
 
 
 
df7e62a
369574e
 
 
 
df7e62a
369574e
df7e62a
369574e
df7e62a
369574e
 
 
df7e62a
369574e
df7e62a
369574e
 
 
df7e62a
369574e
df7e62a
369574e
 
 
 
df7e62a
369574e
 
 
 
 
 
df7e62a
369574e
 
 
 
df7e62a
369574e
df7e62a
369574e
 
 
df7e62a
369574e
df7e62a
369574e
 
 
 
 
 
df7e62a
369574e
df7e62a
369574e
 
 
 
df7e62a
369574e
 
 
 
df7e62a
369574e
df7e62a
369574e
 
 
 
 
 
 
df7e62a
369574e
df7e62a
369574e
 
 
 
df7e62a
369574e
df7e62a
369574e
df7e62a
369574e
df7e62a
369574e
 
 
 
df7e62a
369574e
5b18525
369574e
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
---
license: apache-2.0
title: EmailGuard2
sdk: gradio
emoji: 🌍
colorFrom: blue
colorTo: pink
short_description: The only secure and rational email phishing detector
---
# EmailGuard2 : Advanced Phishing Detection System

A multi-model ensemble system for detecting phishing attempts in emails, URLs, and text messages using AI and feature engineering.

## Features

- Multi-model ensemble prediction
- Advanced feature extraction and analysis
- Real-time phishing detection
- Web-based user interface
- Risk scoring and confidence reporting
- URL and email content analysis

## Installation

1. Clone the repository:
```bash
git clone <repository-url>
cd emailguard-phishing-detection
```

2. Install dependencies:
```bash
pip install -r requirements.txt
```

3. Run the application:
```bash
python app.py
```

4. Open your browser and go to `http://localhost:7860`

## Usage

1. Enter email content, URL, or suspicious text in the input field
2. Click "Advanced Analysis" to process the input
3. Review the results including risk level and confidence scores

## Models Used

- Primary: `cybersectony/phishing-email-detection-distilbert_v2.4.1`
- URL Specialist: Custom URL analysis model
- Feature Engine: Hand-crafted pattern detection rules

## Detection Features

### URL Analysis
- Suspicious domain detection
- Shortened URL identification
- Malicious link patterns

### Content Analysis
- Urgency keyword detection
- Money-related terms
- Personal information requests
- Spelling error patterns
- Excessive capitalization

### Risk Assessment
- HIGH RISK: Strong phishing indicators (>60% confidence)
- MEDIUM RISK: Suspicious patterns (30-60% confidence)
- LOW RISK: Appears legitimate (<30% confidence)

## System Requirements

- Python 3.8+
- 4GB+ RAM
- Internet connection (for initial model download)

## Technical Details

The system uses:
- PyTorch for deep learning models
- Transformers for NLP processing
- Gradio for web interface
- Custom ensemble voting mechanism
- Feature-based risk adjustment

## Example Inputs

**Phishing Example:**
```
URGENT: Your PayPal account has been limited! Verify immediately at http://paypal-security-check.suspicious.com/verify
```

**Legitimate Example:**
```
Hi Sarah, Thanks for the quarterly report. Let's discuss in tomorrow's meeting. Best, Mike
```

## Configuration

Model configuration in `app.py`:
```python
MODELS = {
    "primary": "cybersectony/phishing-email-detection-distilbert_v2.4.1",
    "url_specialist": "cybersectony/phishing-email-detection-distilbert_v2.4.1"
}
```

## Limitations

- This is an educational/research tool
- Always verify suspicious content through official channels
- May produce false positives/negatives
- Requires manual verification for critical decisions

## License

Apache2.0 License

## Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

## Support

For issues and questions, please use the GitHub issue tracker.