Timeline Tools and Visualization

Purpose and Scope

Effective timeline analysis requires appropriate tooling for parsing diverse artifacts, normalizing data, and visualizing complex event sequences. This playbook covers key tools for each stage of timeline construction, from raw evidence to final analysis.

Prerequisites

• Evidence access: Disk images, memory dumps, or log exports to process
• Analysis workstation: System with sufficient resources for processing
• Python environment: Many tools require Python and dependencies
• Storage space: Timeline output can be large for full disk analysis

Artifact Parsing Tools

Plaso (log2timeline)

The primary tool for generating super timelines from disk images:

• Parses 100+ artifact types from Windows, Linux, and macOS
• Outputs to CSV, JSON, or Timesketch format
• Handles file system metadata, event logs, browser history, and more
• Can process disk images (E01, raw) or mounted file systems

Basic usage:

# Create timeline from disk image
log2timeline.py --storage-file timeline.plaso disk_image.E01

# Export to CSV
psort.py -o l2tcsv -w timeline.csv timeline.plaso

Velociraptor

Endpoint visibility and collection platform:

• Collects forensic artifacts from live systems
• Built in artifact library for common evidence types
• Can export directly to timeline format
• Scales to enterprise wide collection

KAPE (Kroll Artifact Parser and Extractor)

Fast triage collection and parsing:

• Collects key artifacts without full disk imaging
• Integrates with various parsers for immediate analysis
• Module based for customization
• Particularly strong for Windows artifacts

Timeline Analysis Platforms

Timesketch

Open source collaborative timeline analysis:

• Web based interface for team analysis
• Imports plaso output directly
• Supports multiple data sources in one investigation
• Collaborative annotation and tagging
• Built in analyzers for pattern detection
• Search and filter capabilities

Key features:

• Sketches for organizing investigations
• Sigma rule integration for detection
• Graph view for relationship visualization
• API for automation

SIEM Platforms

For log based timelines, SIEM tools provide native capabilities:

• Splunk: Timeline visualization app, transaction commands for grouping
• Elastic: Kibana timeline plugin, EQL for event correlation
• Microsoft Sentinel: Investigation graph, timeline view in incidents
• Chronicle: Entity timeline views, procedural search

Visualization Tools

Timeline Explorer

GUI tool for CSV timeline analysis:

• Opens large CSV files efficiently
• Column filtering and sorting
• Highlighting and grouping
• Designed for SANS DFIR CSV formats

Spreadsheet Tools

For smaller timelines or manual correlation:

• Excel or Google Sheets with filtering and conditional formatting
• Pivot tables for aggregation by host or user
• Graphs for visualizing event frequency over time

Custom Visualization

For complex or presentation quality output:

• Python matplotlib/plotly: Custom timeline charts
• Grafana: Dashboard based visualization
• D3.js: Interactive web visualizations

Specialized Parsers

Windows Artifacts

• Eric Zimmerman tools: MFTECmd, PECmd, LECmd, RECmd for specific artifacts
• hayabusa: Windows event log analysis with Sigma rules
• chainsaw: Fast event log searching
• EvtxECmd: Event log parsing to CSV

Memory Forensics

• Volatility 3: Memory image analysis with timeliner plugin
• MemProcFS: Memory analysis as a file system

Network Artifacts

• Wireshark: PCAP analysis with time based filtering
• Zeek: Network log generation from PCAP
• NetworkMiner: Network forensic analysis

Workflow Integration

Collection to Analysis Pipeline

1. Collect: Velociraptor or KAPE for live systems, imaging for offline
2. Parse: Plaso for comprehensive parsing, specialized tools for specific needs
3. Ingest: Import to Timesketch or SIEM
4. Analyze: Search, filter, and annotate
5. Document: Export findings for reporting

Timesketch Pipeline Example

# Collect with Velociraptor
velociraptor-v0.7.0 artifacts collect Windows.KapeFiles.Targets --output collection.zip

# Parse with plaso
log2timeline.py --storage-file case.plaso collection.zip

# Import to Timesketch
timesketch_importer --host https://timesketch.local case.plaso

Tool Selection Considerations

• Scale: How many systems? How much data?
• Collaboration: Solo analysis or team investigation?
• Source type: Disk images vs live collection vs logs only
• Output needs: Interactive analysis vs static report
• Environment: Cloud vs on premise, open source vs commercial

Common Challenges

Performance

• Full disk parsing with plaso can take hours
• Large timelines may overwhelm analysis tools
• Filter data before import when possible
• Use targeted collection instead of full images when appropriate

Data Volume

• A single Windows system can generate millions of timeline events
• Filter by date range and artifact type
• Use iterative analysis: broad then focused

Best Practices

• Test your toolchain before an incident occurs
• Maintain consistent workflows across the team
• Document tool versions and configurations used
• Validate parser output against known good samples
• Keep tools updated for new artifact support

References

• Plaso documentation: plaso.readthedocs.io
• Timesketch: timesketch.org
• Velociraptor: docs.velociraptor.app
• Eric Zimmerman tools: ericzimmerman.github.io
• KAPE documentation: kroll.com/kape
• SANS DFIR resources