New Modular Framework for Email Sender Trust Evaluation

Combining DNS validation, reputation intelligence, and behavioral heuristics for transparent email security

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The Problem: Email as a Persistent Attack Vector

Email remains a cornerstone of digital communication — and one of the most abused. Phishing, spoofing, and impersonation tactics continue to exploit weaknesses in sender authentication. While standards like SPF, DKIM, and DMARC were designed to solve these problems, misconfigurations and incomplete adoption create exploitable gaps.

Commercial email gateways add another layer of defense, but they often fall short in two key ways:

1. Their filtering and scoring systems are opaque — analysts can’t always see why something was flagged.
2. They lack customizability and educational value for smaller teams, researchers, and students.

This is where a modular, transparent approach comes in.

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Introducing the Framework

I developed a Python-based framework for evaluating the trustworthiness of email senders. The goal: to provide a system that is transparent, interpretable, and adaptable — one that blends technical validation with contextual heuristics.

At its core, the framework processes .eml files and evaluates sender risk based on both infrastructure checks and behavioral patterns.

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How It Works

The framework is modular, meaning each component can be used independently or as part of a larger workflow. Key features include:

• Email Parsing → Extracts sender identity and routing metadata from headers
• SPF & MX Validation → Confirms domain hygiene via DNS lookups
• Domain Age Check → Uses WHOIS to measure domain maturity
• IP Reputation Scoring → Integrates with AbuseIPDB for real-time abuse intelligence
• Behavioral Heuristics → Flags risky signals like:
  • Hash-like sender names
  • Suspicious top-level domains (.ru, .cn, etc.)
  • Overly long domain names

From there, the framework applies a scoring model:

• Starts at 100
• Applies structured penalties for missing SPF, invalid MX, short domain age, abuse reports, or suspicious heuristics
• Classifies senders into Low, Medium, or High risk categories

This hybrid model helps reduce false positives while still catching deceptive senders that might look legitimate on the surface.

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Example in Action

Case A: Suspicious Sender (sneezekey.ru)

• SPF valid, MX present, clean IP reputation
• Domain only 108 days old, suspicious display name and .ru TLD
• Final Score: 55 (Medium Risk)

Even though technical checks passed, heuristics flagged the sender — preventing a false sense of security.

Case B: Trusted Sender (comptia.org)

• SPF valid, MX present, clean IP reputation
• Domain ~30 years old, professional display name, trusted .org TLD
• Final Score: 100 (Low Risk)

Here, the system correctly recognized a legitimate sender.

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Use Cases

This framework has applications across multiple domains:

🔹 Security Operations → Triaging suspicious emails with transparent scoring

🔹 Education & Training → Teaching email authentication, DNS, and reputation analysis

🔹 Incident Response → Structured forensic analysis of email metadata

🔹 Automation & Integration → Batch-processing .eml files, enriching SIEM/SOAR platforms, or powering real-time pipelines

Because it’s scriptable and modular, it fits easily into SOC workflows or classroom exercises.

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Limitations & Future Enhancements

Like any framework, there are areas to grow:

• Current model doesn’t yet enforce DKIM or DMARC checks
• Relies on AbuseIPDB as the primary reputation source
• WHOIS data can be incomplete due to registrar privacy policies
• Scoring is rule-based rather than adaptive

Planned enhancements include:

• Adding DKIM/DMARC validation
• Supporting IPv6 and ASN lookups
• Integrating additional reputation sources (VirusTotal, Cisco Talos, IPQualityScore)
• Building a CLI or web dashboard for easier use
• Real-time analysis via Gmail API

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Why This Matters

The key difference between this framework and most commercial solutions is transparency. Every step — from DNS lookups to heuristic checks — is visible and customizable.

For security teams, that means confidence in decision-making.

For students, that means a hands-on way to learn email security.

For researchers, that means a foundation they can extend and experiment with.

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Conclusion

In a landscape where email is both indispensable and vulnerable, we need tools that bridge the gap between raw infrastructure and actionable insight.

This framework offers exactly that: a transparent, modular, and extensible approach to email sender trust evaluation. Whether you’re triaging suspicious emails in a SOC, teaching authentication protocols in a classroom, or experimenting with new heuristics in a lab, it provides clarity and control in a domain often dominated by black-box systems.

Would you like me to also make a less technical, executive-friendly version of this blog post — something targeted at CISOs and business leaders rather than analysts and developers?

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🔗 Continue the Conversation

I also shared a summary of this research on LinkedIn and Medium where I’m discussing how this framework fits into SOC workflows, education, and incident response.

LinkedIn : A Modular Framework for Email Sender Trust Evaluation

Medium : Why Passing SPF and DKIM Isn’t Enough: A New Framework for Email Sender Trust

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Download full paper:

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