🛡️ HyperDecode

High-Confidence Heuristic Engine for Multi-Layer De-obfuscation.
Treating decoding as a probabilistic search problem with near real-time exploration.

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🔍 Overview

HyperDecode treats decoding as a dynamic search problem rather than a static sequence of operations. Unlike traditional tools, it explores a weighted tree of possible decoding paths using a Heuristic Beam Search strategy—simulating a lightweight inference process.

The search process forms a Directed Acyclic Graph (DAG), allowing the engine to recover the original payload across deeply nested and unknown transformation layers with high-confidence accuracy.

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🧠 Design Philosophy

HyperDecode is built on a simple principle:

"If a human can iteratively guess and validate decoding steps, the process can be modeled as a search problem."

By combining heuristic scoring with controlled exploration, HyperDecode automates this human intuition at machine speed. Inspired by search strategies used in AI inference and symbolic execution systems.

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🧬 How It Works (Core Concepts)

1. The Search Workflow (Visual)

graph TD
    Raw["Input Data"] --> Gen{"Candidate Generator"}
    Gen -->|"Decoders (Base64/Hex/XOR)"| S["Score Evaluation"]
    S --> Engine["Scoring Engine"]
    Engine -->|"Heuristic Pruning"| Pool["Beam Search Pool"]
    Pool -->|"Depth Check"| Gen
    Pool -->|"High Confidence"| Result["Ranked Output"]

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2. Theoretical Framework

HyperDecode models de-obfuscation as a traversal through a State Space. For each depth $t$, the search pool $S_{t+1}$ is updated by evaluating all potential successors:

$$S_{next} = \text{TopK}_{s' \in { f_e(s) \mid s \in S, e \in \text{Edges} }} \text{Score}(s')$$

• State Space: Each intermediate output is treated as a node in the transformation graph.
• Transition Function: Decoders act as edges transforming $s \xrightarrow{f_e} s'$.
• Heuristic Function: The Scoring Engine acts as a proxy for semantic understanding, evaluating Shannon entropy, magic numbers, and character distribution.
• Beam Width Control: Limits exploration to the Top-K candidates at each depth to prevent recursive combinatorial explosion.

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🚀 Quick Look: Interaction Trace

When running with the --trace flag, HyperDecode reveals its internal decision-making process:

[Pipeline] Input: "U0dWc2JHOGdhVzRnU0dWNGNHeHZaR1VnUTNScGJtY2dRaFpYSlV4..."
   ├── Level 1: Base64 detected (Score: 0.92) -> "SGVsbG8gaW4gSGV4cXZkZGUgQ3Rpbmc..."
   ├── Level 2: Hex detected    (Score: 0.88) -> "XOR:0x41 decryption sequence..."
   ├── Level 3: XOR (Key:0x41)  (Score: 0.99) -> "HyperDecode Success! { ... }"
[Result] Final Match Found in 12ms.

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✨ Key Features

• 🧠 Heuristic Graph Search: Dynamically explores a transformation DAG using beam search and scoring.
• ⚡ Native Performance: High-speed C core optimized for massive hardware-aware tasks.
• 🔋 Feather-Light: Maintains a <32MB RAM footprint—ideal for professional environments.
• 📋 Recipe System: Design, save, and batch-apply custom transformation chains.
• ⌨️ Colorized CLI: Professional terminal interface with interactive path trace and JSON export.

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📊 Performance Benchmark

Tested on: Intel i5-7200U / 16GB RAM (Multi-threaded v3.2)

Input Complexity	Obfuscation Layers	Time (Avg)	Success
Base64 → Hex → XOR	3	15ms	✅ High
Double Base64 + Rot13	3	35ms	✅ High
Unknown Mixed Encoding	5	140ms	✅ High

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📦 Installation Guide (CLI)

1. Download the latest release from the Releases.
2. Run the automated installer:

   .\install_cli.ps1

3. Restart your terminal to apply PATH changes globally.

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🛤️ Roadmap

• ML Scoring Core: Integrate Tinygrad or ONNX Runtime Core for research-grade scoring.
• Adaptive Beam Width: Dynamically adjust search breadth based on data confidence.
• Scripting Plugin: Lua & Python support for custom transition functions.

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Developed with ❤️ by HyperDecode Team.
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