https://github.com/sumamarahim/phishalert
A phishing email detection System
https://github.com/sumamarahim/phishalert
chrome-extension cybersecurity cybersecurity-tools phishing-detection phishing-protection spam-detection spam-prevention
Last synced: 2 months ago
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A phishing email detection System
- Host: GitHub
- URL: https://github.com/sumamarahim/phishalert
- Owner: sumamarahim
- License: apache-2.0
- Created: 2025-07-23T07:06:45.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-23T07:18:12.000Z (3 months ago)
- Last Synced: 2025-07-23T09:18:21.074Z (3 months ago)
- Topics: chrome-extension, cybersecurity, cybersecurity-tools, phishing-detection, phishing-protection, spam-detection, spam-prevention
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# phishAlert - MVP Documentation
## 1. Project Title
**phishAlert** – A Browser Extension to Detect Phishing and Spam Emails
## 2. Objective
To develop a browser-based extension that automatically analyzes email content in real-time and flags potential phishing and spam emails to protect users from cyber threats.
## 3. Target Users
- General Gmail users (initially)
- Non-technical individuals vulnerable to phishing
- Small businesses without dedicated cybersecurity tools
## 4. Core Problem Statement
Phishing and spam emails are becoming increasingly sophisticated and frequent. Many users unknowingly fall victim due to a lack of awareness or proper detection tools. Current email platforms do not always detect every malicious email.
## 5. Proposed Solution (MVP Scope)
A Chrome browser extension that:
- Scans the content of opened Gmail emails.
- Detects phishing or spam content using AI or heuristic techniques.
- Displays a visual warning if the email is suspicious.
- Operates locally with minimal permissions.
## 6. Key MVP Features
| Feature | Description |
|--------|-------------|
| **Email DOM Extraction** | Extract subject, body, sender from opened Gmail emails. |
| **Phishing/Spam Detection Engine** | Run AI-based or regex-based checks for known patterns (e.g., fake links, urgent language, spoofed addresses). |
| **Real-Time Warning Display** | Show warning banner or icon if email is suspicious. |
| **User Feedback Option** | Allow users to confirm or correct detection (optional for MVP). |
| **Background Script** | Handles API communication and permission management. |
## 7. Non-MVP (Future) Features
- Integration with external APIs (e.g., VirusTotal, Google Safe Browsing)
- Detailed phishing/spam explanation to user
- Dashboard with statistics
- Multi-email provider support (Outlook, Yahoo)
- User training or reporting module
## 8. Technology Stack
| Layer | Tool/Framework |
|-------|----------------|
| **Frontend** | JavaScript, HTML/CSS, Chrome Extension APIs |
| **AI/Detection** | OpenAI API / Custom ML Model (fallback to regex rules) |
| **Backend (optional)** | Firebase/Supabase for feedback logging (optional) |
| **Communication** | `chrome.runtime` for background-foreground messaging |
## 9. User Flow (MVP)
1. User opens Gmail.
2. Extension detects email opened.
3. Extension extracts email data.
4. AI/regex engine processes the data.
5. If threat is found, warning banner appears.
6. User closes or ignores the warning.
## 10. Success Criteria
- Can detect basic phishing patterns with >70% accuracy.
- Seamless integration into Gmail UI.
- No lag or blocking behavior in Gmail.
- Warning is visible and understandable by general users.
## 11. Constraints and Assumptions
- Gmail DOM structure may change—must be flexible.
- Users will grant minimal permissions.
- Limited usage of OpenAI or external APIs due to cost (can fall back to regex).
## 12. Initial Development Timeline
| Week | Milestone |
|------|-----------|
| 1 | Setup basic Chrome extension structure |
| 2 | DOM scraping and Gmail email detection |
| 3 | Implement AI-based or regex-based detection logic |
| 4 | Display phishing warning UI |
| 5 | Test across email scenarios |
| 6 | Final bug fixing, packaging, and documentation |
## 13. Open Questions
- Should detection happen entirely client-side or also involve a backend?
- What fallback detection to use if AI fails?
- What metrics/logs should be collected for improvement?