🛡️ CyberX | DFIR Framework

End-to-End Digital Forensics & Incident Response Framework

Case Management • Evidence Processing • Artifact Parsing • Timeline Analysis • Incident Correlation

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📌 Overview

CyberX DFIR Framework is a modular Digital Forensics and Incident Response (DFIR) platform built using Flask.

The framework provides a unified investigation workspace for:

📁 Case Management
📤 Evidence Upload & Processing
🔍 Artifact Analysis
🕒 Timeline Reconstruction
🚨 Incident Detection & Correlation
📊 Investigation Data Export

All forensic artifacts are normalized into a common Event Model, allowing investigators to analyze artifacts across:

• Events Explorer
• Timeline View
• Incident Dashboard

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🔎 Artifact Analysis Modules

Artifact	Extensions	Parser / Engine	Output
✅ Windows Event Logs	.evtx	Hayabusa + Sigma Rules	Threat detections, suspicious activities
✅ Memory Dumps	.raw, .mem, .dmp, .vmem	Volatility3 + IOC Extraction	Processes, network artifacts, memory analysis

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🏗️ DFIR Processing Pipeline

                Evidence Upload
                       |
                       ↓
              Artifact Identification
                       |
        +--------------+--------------+
        |              |              |
      EVTX        Registry       Memory
        |              |              |
    Hayabusa      Hive Parser   Volatility3
        |              |              |
        +--------------+--------------+
                       |
                       ↓
              Normalized Event Model
                       |
          +------------+------------+
          |                         |
       Timeline              Incident Engine
          |                         |
          ↓                         ↓
 Investigation View          Threat Detection

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⚙️ Installation

Clone Repository

git clone https://github.com/itsmeRiF/dfir-framework2.git

cd dfir-framework2

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Create Virtual Environment

python -m venv .venv

.venv\Scripts\activate

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Install Requirements

pip install -r requirements.txt

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Create user

python bootstrap.py

Download Hayabusa and place:

hayabusa.exe

inside:

tools/

Before first use:

cd tools

hayabusa.exe update-rules

This downloads:

• Sigma Detection Rules
• Detection Metadata
• Hayabusa Rule Configuration

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Start Application

python app.py

Access:

http://127.0.0.1:1338

Default Credentials:

Username: analyst
Password: analyst123

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🗺️ Development Roadmap

Phase 1 — Core Windows Artifacts ✅

• Windows Event Logs (EVTX)
• Memory Dumps

To-do:

• Display summary of RAM Analysis
• Running processes
• Active network connections

Phase 2 — Advanced Artifact Support 🚧

• Browser History
• Registry Hives
• Prefetch Files
• Jump Lists

Phase 3 — File System Forensics 🔮

• Master File Table (MFT)
• USN Journal
• SRUM Database
• Recycle Bin Analysis

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🚀 Current Features

✅ Case Management
✅ Evidence Repository
✅ Artifact Auto Detection
✅ Hayabusa Integration
✅ Sigma Rule Detection
✅ Event Normalization
✅ Timeline Analysis
✅ Incident Correlation
✅ Severity Classification
✅ CSV Export

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🔮 Future Integrations

• MITRE ATT&CK Mapping
• Threat Intelligence Integration
• IOC Extraction Engine
• Automated Investigation Reports

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👥 Contributors

Thanks to all contributors who helped build and test this CyberX DFIR Framework.

Made with ❤️ in India 🇮🇳

CyberX DFIR Framework