# malrev/ABD

Course materials for Advanced Binary Deobfuscation by NTT Secure Platform Laboratories

Repository: https://github.com/malrev/ABD
Canonical: https://ross.abutalabs.com/products/abd
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Last push: 2020-11-14T16:29:48+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2395, "days_push": 2118, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1167, forks 174 (observed 2026-08-28T04:03:50.617818+00:00)

## What it is
Course materials for Advanced Binary Deobfuscation taught by NTT Secure Platform Laboratories at GCC Tokyo 2020. It covers obfuscation principles, data-flow analysis, and SAT/SMT-based binary analysis with hands-on Jupyter Notebook exercises using Miasm and Z3.

## Use cases
- learn binary deobfuscation techniques
- study symbolic execution for reverse engineering
- analyze obfuscated malware
- build custom binary analysis tools
- practice data-flow analysis on binaries
- learn to use Miasm and Z3 for deobfuscation

## When to choose
- you are a security analyst wanting to deobfuscate binaries
- you want structured hands-on training in obfuscated code analysis
- you need to analyze obfuscated malware from APT campaigns

## When to avoid
- you lack x86/x64 assembly and low-level CS fundamentals
- you need a production deobfuscation tool rather than learning materials
- you want actively updated course content

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: reverse-engineering, security, developer-tools
- domain: security, reverse-engineering, education, tutorials
- platform: python, cross-platform
- tags: deobfuscation, binary-analysis, malware-analysis, symbolic-execution, course-materials, jupyter-notebook, miasm, z3, linux

## Member repositories
- malrev/ABD (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:50.617818+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:28:52.328025+00:00, confidence not recorded.
  - readme: https://github.com/malrev/ABD (fetched 2026-08-28T04:03:50.617818+00:00, sha 26f5aa88b710)
- Data as of 2026-08-30T08:39:29.467469+00:00.