Email Header Analyzer for Phishing Detection - Technical & Engineering Guide
1. Introduction
1.1 Purpose
This guide describes the design and implementation of an Email Header Analyzer tool to detect phishing emails. The tool parses email headers to identify anomalies indicative of phishing attempts.
1.2 Scope
The tool is useful for organizations and individuals to enhance email security by detecting and flagging potential phishing emails before they reach end-users.
1.3 Definitions & Acronyms
|
Acronym |
Definition |
|
SMTP |
Simple Mail Transfer Protocol |
|
IP Address |
Internet Protocol Address |
|
SPF |
Sender Policy Framework |
|
DKIM |
DomainKeys Identified Mail |
|
DMARC |
Domain-based Message Authentication, Reporting, and Conformance |
2. System Architecture
The architecture of the Email Header Analyzer includes:
- **Input Module**: Extracts email headers for analysis.
- **Parsing Engine**: Identifies and parses key header fields such as SPF,
DKIM, and DMARC records.
- **Anomaly Detection Module**: Applies rules and heuristics to detect
suspicious patterns.
- **Output Module**: Provides a detailed report highlighting potential phishing
indicators.
3. Key Features
3.1 Header Parsing
Extracts critical information such as sender address, server IP, and authentication records from email headers.
3.2 SPF/DKIM/DMARC Validation
Validates email sender's authenticity using SPF, DKIM, and DMARC policies.
3.3 Anomaly Detection
Detects mismatched domains, spoofed addresses, and unusual IP addresses.
4. Implementation Steps
1. **Setup Development Environment**: Install necessary
libraries such as `email` and `re` in Python.
2. **Header Extraction**: Implement functionality to extract headers from raw
email data.
3. **Parsing Logic**: Write algorithms to parse and validate header fields.
4. **Anomaly Rules**: Define rules for detecting common phishing patterns.
5. **Reporting Interface**: Develop a user-friendly interface to display
analysis results.
6. **Testing and Deployment**: Validate the tool with real-world phishing and legitimate
email samples.
5. Security Considerations
1. Handle raw email data securely to prevent accidental
leaks.
2. Use up-to-date rules and heuristics to detect emerging phishing techniques.
3. Log suspicious findings for further review and refinement.
6. Tools and Technologies
- **Programming Language**: Python
- **Libraries**: email, re, ipaddress, dnspython
- **Database**: SQLite for storing parsed headers and analysis results
- **UI Framework**: Flask or Django for a web-based interface
7. Testing and Validation
1. Validate the tool's accuracy with known phishing and
legitimate emails.
2. Test scalability with bulk email samples.
3. Ensure the tool handles diverse email formats and encodings.