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Memory-Driven Forensic Analysis of SQL Server: A Buffer Pool and Page Inspection Approach.

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  • 1Police Science Institute, Korean National Police University, Asan 31539, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a memory-based forensic method for real-time deleted data recovery in Microsoft SQL Server. It enables prompt recovery of critical data from memory, enhancing digital forensics for IoT and edge computing systems.

Keywords:
SQL serverbuffer poolcachedatabase forensicsdeleted data recoverysensor-based systemstransaction log

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Area of Science:

  • Computer Science
  • Digital Forensics
  • Database Systems

Background:

  • Traditional deleted data recovery methods in Microsoft SQL Server (transaction log analysis, offline file examination) lack real-time applicability and miss volatile memory data.
  • Sensor-driven systems (IoT gateways, edge computing) store critical data locally in lightweight SQL engines, making them vulnerable to insider threats.
  • Real-time forensic analysis is crucial for time-sensitive incidents like cyber intrusions and insider manipulation.

Purpose of the Study:

  • To propose a memory-based forensic procedure for real-time recovery of deleted data in Microsoft SQL Server.
  • To address the limitations of traditional methods in capturing volatile, memory-resident data.
  • To enhance digital forensics capabilities for IoT and edge computing environments.

Main Methods:

  • Capturing deletion event information via transaction log analysis immediately post-deletion.
  • Directly inspecting memory-resident pages within the server's Buffer Pool.
  • Analyzing Buffer Pool page structures and cross-referencing with log data to establish a memory-driven forensic framework.

Main Results:

  • Successful partial or full recovery of deleted records in a live SQL Server environment using combined transaction log and in-memory page analysis.
  • Demonstrated feasibility of real-time forensic analysis without interrupting the operational database.
  • Established a memory-driven framework for recovery and verification of deleted records.

Conclusions:

  • The proposed memory-based methodology enhances the speed and accuracy of digital forensics in time-sensitive scenarios.
  • Real-time deleted data recovery from memory is critical for IoT sensor data integrity, forensic traceability, and system resilience.
  • This approach provides a foundational methodology for prompt and precise recovery of deleted data, crucial for insider threat detection and cyber intrusion response.