timeline-forensics

Build a super timeline by aggregating data from disk images, memory, logs, PCAPs, and registry hives to reveal event sequences and correlations. The skill supports super timelines, anomaly detection, and visualizations for incident response and digital forensics.

Yes, you can correlate events across disk images, memory, logs, PCAPs, and registry hives. This multi-source correlation aligns events across artifacts to reveal relationships and pivot points for incident response analysis.

Yes, building forensic timelines requires Python 3.9+. You also need optional libraries such as plaso, pandas, and plotly for parsing artifacts and generating timeline visualizations and outputs.

Reconstruct incident chronology by aggregating artifacts from disks, memory, logs, networks, and registries into a unified forensic timeline. This approach maps incident progression to identify the initial compromise window and key milestones.

Yes, timeline investigation supports visualization and reporting. You can generate timelines and export reports for case reviews, using libraries like plotly to visualize the incident progression and key milestones.

Anomaly detection during forensic timeline analysis works by aggregating multi-source data into a unified timeline, allowing investigators to identify unusual event sequences and correlations that indicate potential compromise or malicious activity.