Analyzing Email Headers For Phishing Investigation
Overview
Cybersecurity skill for analyzing email headers for phishing investigation. Follows industry best practices and security standards.
When to Use
Trigger phrases:
"analyzing email headers for phishing investigation"
"Parse and analyze email headers to trace the origin of phishing emails, verify s"
When investigating a suspected phishing email to determine its true origin
For verifying sender authenticity and detecting email spoofing
During incident response when a user has clicked a phishing link
When tracing the delivery path and relay servers of a suspicious email
For validating SPF, DKIM, and DMARC alignment to identify forgery
When NOT to Use
- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope
Prerequisites
- Raw email headers from the suspicious message (EML or MSG format)
- Understanding of SMTP protocol and email header fields
- Access to DNS lookup tools (dig, nslookup) for SPF/DKIM/DMARC verification
- Email header analysis tools (MHA, emailheaders.net concepts)
- Python with email parsing libraries for automated analysis
- Access to threat intelligence platforms for IP/domain reputation
Workflow
# Example: IOC detection
import re
IOC_PATTERNS = {
"ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
"domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
"hash_md5": r"\b[a-f0-9]{32}\b",
"hash_sha256": r"\b[a-f0-9]{64}\b",
}
def extract_iocs(text: str) -> dict:
return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
- Scope the Analysis — Define what email headers artifacts or data sources to examine and the investigation timeline.
- Preserve Evidence — Create forensic copies of relevant data. Maintain chain of custody documentation.
- Extract Key Indicators — Use phishing investigation to parse and extract relevant email headers data points from collected artifacts.
- Correlate Findings — Cross-reference extracted data with other sources (threat intel, logs, timelines).
- Build Timeline — Construct a chronological sequence of events related to email headers.
- Document Analysis — Write findings report with evidence, conclusions, and recommendations.
Tools
- phishing investigation — Primary tool for this skill
- Forensic Toolkit — Evidence collection and analysis
- Timeline Tools — Chronological event reconstruction
- Log Analysis Platform — Centralized log parsing and search
Process
- Reconnaissance — Gather target information, identify attack surface, enumerate services
- Analysis/Exploitation — Execute the technique, analyze results, document findings
- Reporting — Document IOCs, write findings, provide remediation recommendations
Verification
Anti-Rationalization Table
| Rationalization |
Reality |
| "We are too small to be targeted" |
Automated attacks target everyone. Size does not matter. |
| "Security slows us down" |
A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" |
Vulnerabilities in production are exploited within hours. Fix before deploy. |
1---2name: analyzing-email-headers-for-phishing-investigation3description: Use when parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation. Use when working with analyzing email headers for phishing investigation.4license: Apache-2.05---67# Analyzing Email Headers For Phishing Investigation89## Overview1011Cybersecurity skill for analyzing email headers for phishing investigation. Follows industry best practices and security standards.1213## When to Use14**Trigger phrases:**15- "analyzing email headers for phishing investigation"16- "Parse and analyze email headers to trace the origin of phishing emails, verify s"1718- When investigating a suspected phishing email to determine its true origin19- For verifying sender authenticity and detecting email spoofing20- During incident response when a user has clicked a phishing link21- When tracing the delivery path and relay servers of a suspicious email22- For validating SPF, DKIM, and DMARC alignment to identify forgery232425## When NOT to Use2627- When you lack proper authorization for testing28- For production systems without change management29- When the task requires legal or compliance expertise beyond technical scope303132## Prerequisites33- Raw email headers from the suspicious message (EML or MSG format)34- Understanding of SMTP protocol and email header fields35- Access to DNS lookup tools (dig, nslookup) for SPF/DKIM/DMARC verification36- Email header analysis tools (MHA, emailheaders.net concepts)37- Python with email parsing libraries for automated analysis38- Access to threat intelligence platforms for IP/domain reputation3940## Workflow4142```python43# Example: IOC detection44import re4546IOC_PATTERNS = {47 "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",48 "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",49 "hash_md5": r"\b[a-f0-9]{32}\b",50 "hash_sha256": r"\b[a-f0-9]{64}\b",51}5253def extract_iocs(text: str) -> dict:54 return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}55```56571. **Scope the Analysis** — Define what email headers artifacts or data sources to examine and the investigation timeline.582. **Preserve Evidence** — Create forensic copies of relevant data. Maintain chain of custody documentation.593. **Extract Key Indicators** — Use phishing investigation to parse and extract relevant email headers data points from collected artifacts.604. **Correlate Findings** — Cross-reference extracted data with other sources (threat intel, logs, timelines).615. **Build Timeline** — Construct a chronological sequence of events related to email headers.626. **Document Analysis** — Write findings report with evidence, conclusions, and recommendations.6364## Tools6566- **phishing investigation** — Primary tool for this skill67- **Forensic Toolkit** — Evidence collection and analysis68- **Timeline Tools** — Chronological event reconstruction69- **Log Analysis Platform** — Centralized log parsing and search707172## Process73741. **Reconnaissance** — Gather target information, identify attack surface, enumerate services751. **Analysis/Exploitation** — Execute the technique, analyze results, document findings761. **Reporting** — Document IOCs, write findings, provide remediation recommendations7778## Verification7980- [ ] All email headers procedures executed completely and documented81- [ ] Findings validated against multiple data sources82- [ ] False positives identified and filtered83- [ ] Results documented with evidence and timestamps84- [ ] Recommendations provided with risk-based prioritization8586## Anti-Rationalization Table8788| Rationalization | Reality |89|---|---|90| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |91| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |92| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |