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FOREST: Inspecting and Tracking RESTful APIs for Constructing a Cloud Forensic Knowledge Base

Introduction

Modern cloud platforms rely heavily on RESTful APIs to manage communication between users and services. Applications such as cloud storage, collaboration tools, and enterprise communication systems continuously exchange data through these APIs.

However, in digital forensic investigations, these APIs introduce significant challenges:

  • Many APIs are undocumented

  • API structures change frequently without public notice

  • Forensic-relevant data may bypass traditional logging mechanisms

  • Evidence acquisition becomes difficult to reproduce and verify

To address these issues, we introduce FOREST — a framework designed to systematically inspect, analyze, and track RESTful APIs in cloud environments.

The Problem in Cloud Forensics

Traditional forensic methods were designed for static systems. In contrast, cloud ecosystems are:

  • Dynamic


  • Frequently updated

  • API-driven

  • Service-version dependent

This creates two major forensic risks:

  1. Hidden Evidence Exposure
    Undocumented APIs may expose valuable forensic artifacts such as metadata, timestamps, user activity logs, or file histories.

  2. Non-Reproducible Evidence Collection
    When API structures change, investigators may not be able to replicate evidence acquisition in future examinations, weakening legal reliability.

Introducing FOREST

FOREST (Forensic Observation of RESTful Services and Tracking) is an automated framework that:

✔ Monitors live API traffic
✔ Identifies undocumented API endpoints
✔ Extracts artifact-bearing responses
✔ Generates OpenAPI Specifications
✔ Tracks schema changes over time
✔ Analyzes parameter dependencies

FOREST transforms dynamic cloud API behavior into a structured forensic knowledge base.

How FOREST Works

1️⃣ Live Traffic Analysis

FOREST captures API requests generated through natural user interactions, ensuring realistic and investigation-relevant data.

2️⃣ Undocumented Endpoint Discovery

It identifies APIs not officially documented by service providers, revealing hidden forensic artifacts.

3️⃣ OpenAPI Specification Generation

FOREST automatically generates structured API documentation for forensic reproducibility.

4️⃣ Longitudinal Schema Tracking

The framework compares API versions over time, detecting structural changes that may affect evidence acquisition.

Experimental Evaluation

FOREST was evaluated on real-world cloud platforms:

  • Microsoft OneDrive

  • Microsoft Teams

  • Mattermost

Key Findings:

  • Successfully uncovered undocumented APIs

  • Identified structural API changes across versions

  • Enabled reproducible evidence acquisition

  • Supported reliable forensic analysis in dynamic cloud environments

The results demonstrate that FOREST significantly enhances cloud forensic intelligence capabilities.

Why FOREST Matters

As cloud services evolve, forensic investigators need tools that:

  • Adapt to dynamic environments

  • Detect hidden data exposure

  • Maintain evidentiary integrity

  • Support reproducibility in court

FOREST provides a scalable and systematic approach to building a Cloud Forensic Knowledge Base through API monitoring and tracking.

Conclusion

Cloud investigations can no longer rely solely on traditional forensic techniques. With the rapid evolution of RESTful APIs, automated tracking and structured documentation are essential.

FOREST bridges this gap by transforming dynamic API behavior into verifiable forensic intelligence — ensuring that digital evidence remains discoverable, reproducible, and legally defensible in cloud-centric investigations.

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