MemTraceDB: Forensic Memory Analysis

• MemTraceDB is a forensic system that reconstructs MySQL user activity timelines by directly analyzing volatile memory artifacts.
• It employs the ActiviTimeTrace algorithm to extract and correlate connection blocks, executed query lists, and global query stacks from memory snapshots.
• Empirical findings on query stack limits guide snapshot scheduling, ensuring evidentiary completeness and robust audit trails in high-risk environments.

MemTraceDB is a forensic analysis system enabling the reconstruction of user activity timelines for MySQL database processes through direct examination of volatile memory artifacts. It bypasses conventional disk-based audit or transaction logs, which are susceptible to tampering by privileged insiders or external attackers, and leverages the inherent reliability of in-memory evidence to establish ground-truth sequences of user actions. The system applies the ActiviTimeTrace algorithm to raw memory snapshots, extracting and correlating forensic artifacts—including user connection blocks, executed query lists, and the global query stack—to reconstruct forensically sound, chronological records of activity. A critical empirical observation is the finite operational capacity of the MySQL query stack, permitting robust, data-driven guidelines for snapshot acquisition frequencies that ensure evidentiary completeness and reliability, independent of potentially compromised disk-based logging infrastructures.

1. Forensic Motivation and Scope

MemTraceDB addresses the vulnerability of disk-based audit and transaction logs to tampering, purging, or disabling by individuals with elevated privileges. In high-risk contexts—such as healthcare, finance, and regulated commerce—this introduces significant forensic blind spots and undermines accountability requirements under frameworks such as GDPR and HIPAA. Memory analysis provides a privileged artifact source: volatile data structures within the live MySQL process contain granular records of recent user activity, including connection states, query execution trails, and process context information. MemTraceDB operationalizes this premise, enabling investigators to circumvent tampered logs and reconstruct comprehensive user activity timelines directly from memory snapshots (Nissan, 7 Sep 2025).

2. Artifact Extraction Methodology

The forensic workflow implemented by MemTraceDB proceeds in two principal stages:

A. Acquisition

• Memory snapshots of the MySQL server process are captured using methods suitable to the environment: Procdump for Windows platforms, direct segment copying from /proc/[pid]/mem for Linux, or full-system VM memory dumps (as supported by Volatility).
• Snapshots in the range of 2.1–2.6 GB are typical under moderate workloads; larger captures occur under heavy transaction rates.

B. Extraction

• MemTraceDB uses deterministic, offset-based carving to locate connection blocks, query artifacts, and system data:
  • Connection blocks are spaced at fixed intervals of 4,352 bytes.
  • Connection and system info reside at well-known offsets (e.g., 3,579 bytes for user connection details).
  • The ten most recent queries for each user and the monolithic global query stack are extracted using further predefined memory offsets.
• Extracted artifacts populate four principal lists: connection metadata (usernames, IPs, databases, last query), system attributes (process and OS identifiers), granular query lists (last ten per user), and the global, process-wide query stack.

This extraction technique ensures that user connection records, executed queries, and operational context are retrieved systematically for subsequent reconstruction.

3. ActiviTimeTrace Algorithm

The ActiviTimeTrace algorithm forms the analytical core of MemTraceDB’s timeline reconstruction capability. Its operational steps are:

• Inputs: extracted forensic artifacts—user connections, user system info, per-user query lists, and process-wide query stack.
• Procedure:
  1. Authenticate user connections by matching login sequences and verifying systemic details (IP, database, allowed configurations).
  2. Systematically correlate “last executed query” with the ten recent queries and the global query stack, mapping queries to connection slots per user.
  3. Compile a timeline tuple for each user, concatenating system metadata, session-specific connection information, and a fully ordered query execution sequence.

The algorithm’s comprehensive concatenation and verification yield a timeline closely reflecting the actual history of each user’s session, even when disk logs have been disabled or manipulated (Nissan, 7 Sep 2025).

4. Empirical Limits of MySQL Query Stack and Snapshot Scheduling

Experimental evaluation established that the underlying MySQL query stack maintains a fixed capacity, empirically observed at approximately 9,997 queries. Upon reaching this threshold, the oldest query in the stack is overwritten by the most recent, while the remainder persist unchanged. This has two direct forensic implications:

• Attribution Ambiguity: In multi-user circumstances, when cumulative query counts surpass 9,997, precise mapping of queries to individual connections becomes intractable, as older queries are lost to overwriting.

• Snapshot Frequency: To ensure evidentiary continuity, MemTraceDB introduces explicit formulas, presented in LaTeX, for scheduling memory captures:

  • Per-user query limit: $Q_n = \frac{9995}{n}$, where $n$ is the number of users.
  • Snapshot interval: $t_{snapshot} = \frac{9997}{3 \times n}$, with $t_{snapshot}$ in minutes, and a per-user query rate of three per minute.
  • For 10 concurrent users ($3 \times 10 = 30$ queries/minute), snapshots every ~333 minutes maintain comprehensive query traceability.

This approach provides a concrete, data-driven guideline for evidence acquisition in forensic audit protocols (Nissan, 7 Sep 2025).

5. Practical Applications and Limitations

MemTraceDB has direct utility in a range of forensic scenarios:

• Robust Timeline Reconstruction: Investigators can recover a detailed activity log (connections, queries, session attributes) despite the presence of compromised or missing disk-based logs.
• Regulatory and Compliance Auditing: Especially relevant in finance, healthcare, and commercial sectors adhering to strict data provenance requirements.
• Scalability and Stability: The system has been validated in both remote (networked, multi-VM) and local (multi-user per host) configurations, but exhibits instability above 40 concurrent users and attribution ambiguity when cumulative query counts breach stack capacity.
• Artifact Integrity: Partial corruption of query data within memory snapshots can sometimes occur, requiring careful validation of extraction completeness.

A plausible implication is that very high transaction rate environments may require frequent or continuous snapshotting, and that artifact extraction must account for possible corruption or noise (Nissan, 7 Sep 2025).

6. Comparative Context: Memory Tracing Systems

While MemTraceDB is specialized for forensic user activity reconstruction in database contexts, it shares methodological affinities with broader memory tracing frameworks:

• HMTT (“Hybrid Memory Trace Tool”) (Bao et al., 2011): Employs a hybrid hardware/software strategy to bridge the semantic gap in low-level memory traces via DIMM-snooping and software-driven semantic injection (“I-Codes” in reserved configuration space). Focus is on capturing full-system traces for architectural and OS research; semantic event association via hardware and software co-design.
• DINAMITE (“Dynamic INstrumentation and Analysis for MassIve Trace Exploration”) (Miucin et al., 2016): Uses compile-time instrumentation via LLVM passes, producing high-fidelity memory access traces and supporting scalable, real-time analytics via Spark Streaming for profiling and optimization.
• Both leverage either raw memory instrumentation or trace extraction for performance and forensic analysis, though MemTraceDB is uniquely tailored for database process investigation with explicit snapshot scheduling formulas and forensic validation protocols.

7. Future Directions

MemTraceDB’s development demonstrates the viability of in-memory analysis as a complement or alternative to disk-based audit logging. Immediate extensions include:

• Applicability to other database systems with analogous in-memory artifact structures.
• Integration of memory-derived timelines with persistent storage evidence and conventional logs, supporting multi-source, cross-validated forensic frameworks.
• Optimizing scaling and timeline attribution under higher concurrency, potentially through adaptive snapshot rates and artifact redundancy.
• Investigating artifact corruption mitigation measures and reliability improvements in volatile memory extraction.

This suggests further research will focus on unifying memory forensics with traditional methods, enhancing forensic intelligence and evidentiary reliability in complex security environments (Nissan, 7 Sep 2025).

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MemTraceDB exemplifies the use of memory forensics as a robust, empirically validated method for reconstructing database user activity, establishing new standards of accountability and evidence resilience in adversarial or compromised systems.