AnubisX Framework

A Scientific Methodology for Behavioral Identity Attribution

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Research Identity

The AnubisX Framework is a formal scientific methodology for determining the identity of a human operator from their digital behavioral patterns. Unlike traditional attribution methods that rely on transient technical artifacts — IP addresses, device fingerprints, browser configurations — the framework measures persistent human cognitive signatures that are rooted in stable cognitive processing habits.

DOI: 10.5281/zenodo.21446923
Figshare: 10.6084/m9.figshare.33028817
Repository: https://github.com/AnubisXFramework/AnubisXFramework
Website: https://anubisxframework.github.io
Mirror: https://anubisxframework.nullc0d3.workers.dev

Original Framework: Ahmed Awad (NullC0d3)
Original Research: Ahmed Awad (NullC0d3)
Author Website: https://ahmedawadresearch.github.io
Contact: anubisxframework@gmail.com

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Research Motivation

Digital attribution — linking digital actions to their human source — is a foundational capability in cyber threat intelligence, digital forensics, and national security. Current methodologies rely primarily on technical artifacts that sophisticated adversaries can spoof or eliminate. Behavioral attribution offers an alternative paradigm: analyzing intrinsic patterns of human cognition observable through digital traces.

The framework addresses three specific limitations:

1. Fragmentation — Behavioral attribution methods are developed independently across disciplines without unifying theory
2. Methodological rigor — Most work lacks formal, pre-specified validation criteria
3. Transparency — Comprehensive documentation of limitations and failure modes is uncommon

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Framework Concept

┌────────────────────────────────────────────────────────────┐
│                   Decision Layer                            │
├────────────────────────────────────────────────────────────┤
│                   Evidence Layer                            │
├────────────────────────────────────────────────────────────┤
│                  Comparison Layer                           │
├────────────────────────────────────────────────────────────┤
│                  Profile Layer                              │
├────────────────────────────────────────────────────────────┤
│                  Feature Layer                              │
├────────────────────────────────────────────────────────────┤
│                    Data Layer                               │
└────────────────────────────────────────────────────────────┘

The framework employs a layered architecture for behavioral evidence processing, supporting multiple analytical workflows and integrating evidence from diverse behavioral modalities.

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Scientific Contribution

The framework advances behavioral identity attribution through:

1. Theoretical Innovation — A comprehensive axiomatic foundation establishing the logical basis for behavioral attribution
2. Methodological Rigor — A formal validation framework with pre-specified acceptance criteria
3. Multi-Modal Evidence Integration — A principled approach to fusing evidence from multiple behavioral sources
4. Empirical Investigation — Proof-of-concept validation on real-world data

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Repository Contents

Directory	Contents
Documentation/	Public documentation, theory, architecture overview
Theory/	Theoretical foundation documents
Mathematics/	Mathematical foundations
Validation/	Validation framework and methodology
Whitepaper/	Complete whitepaper documents
Journal/	Journal revision and publication materials
Algorithms/	Algorithm specifications (conceptual)
Research/	Research methodology and questions
API_Docs/	API reference documentation (conceptual)
Citation/	Citation metadata and guides

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Persistent Identifiers

Platform	Identifier
DOI	10.5281/zenodo.21446923
Figshare	10.6084/m9.figshare.33028817
ORCID	0009-0005-0654-3393
GitHub	AnubisXFramework

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How to Cite

@software{anubisx2026framework,
  title = {AnubisX Framework: A Scientific Methodology for Behavioral Identity Attribution},
  author = {Awad, Ahmed},
  year = {2026},
  url = {https://github.com/AnubisXFramework/AnubisXFramework},
  doi = {10.5281/zenodo.21446923},
  license = {CC-BY-4.0}
}

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Academic Profiles

Platform	Profile
ORCID	0009-0005-0654-3393
LinkedIn	/in/nullc0d3
ResearchGate	/profile/Ahmed-Awad-118
Figshare	/authors/Ahmed_Awad/24415733

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License

This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

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© 2026 Ahmed Awad (NullC0d3). All rights reserved.

Classification: PUBLIC (C0)