Email Header Analyzer for Phishing Detection

 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.