Autonomous Forensic Evidence Correlation Framework

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  Coding Workspace

Overview

Protocol SIFT is a forensic investigation framework designed to automate the analysis of memory forensics artifacts generated from Volatility.

Traditional incident response workflows require analysts to manually review multiple outputs such as process listings, network connections, DLL loads, and memory injection findings. Protocol SIFT streamlines this process by correlating evidence across artifacts, assigning risk scores, and generating explainable findings.

The project demonstrates how structured forensic data can be transformed into an autonomous investigation pipeline.

Problem Statement

Memory forensics investigations often generate large volumes of data spread across multiple Volatility plugins.

Analysts must manually correlate:

• Running processes
• Process hierarchies
• Network activity
• DLL loads
• Memory injection indicators

This process is time-consuming and prone to oversight.

Protocol SIFT automates evidence aggregation and prioritization to accelerate incident response investigations.

Architecture

Memory Dump

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Volatility Framework

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Artifact Extraction

pslist pstree netscan dlllist malfind

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Structured Artifact Repository

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Protocol SIFT Analysis Engine

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Evidence Correlation

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Risk Scoring

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Self-Validation Loop

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Investigation Report

Dataset Preparation

Artifacts are generated using Volatility 3:

• vol -f memory.raw windows.pslist > pslist.txt

• vol -f memory.raw windows.pstree > pstree.txt

• vol -f memory.raw windows.netscan.Netscan > netscan.txt

• vol -f memory.raw windows.dlllist > dlllist.txt

• vol -f memory.raw windows.malware.malfind > malfind.txt

These artifacts become the investigation dataset consumed by Protocol SIFT.

Features

• Automated artifact ingestion
• Process correlation
• Network activity detection
• Memory injection detection
• Suspicious DLL identification
• Risk scoring engine
• Evidence collection
• Execution trace logging
• Self-validation workflow

Current Limitations

• Volatility execution is performed separately from the analysis engine.
• AI-assisted reasoning is planned for future releases.
• Live memory acquisition is not currently supported.
• Detection logic currently relies on rule-based correlation.

Future Work

• LLM-powered investigation summaries
• Dynamic tool orchestration
• Automatic Volatility execution
• Attack-chain reconstruction
• Confidence scoring enhancements
• Integration with incident response platforms

Conclusion

Protocol SIFT demonstrates an approach to autonomous forensic evidence correlation by transforming raw Volatility artifacts into actionable investigation findings. The framework reduces manual analysis effort while maintaining transparency through evidence-backed reporting and execution tracing.

Built With

• cybersecurity
• protocol
• python
• sans
• shift-platform