Automated Forensic Timeline Scripting · Hacking Project

Automate defensible super-timeline generation, enrichment, and analysis to accelerate incident reconstruction.

**The problem**
Building a forensic timeline manually — correlating filesystem MACB times, event logs, registry, browser history, and application artifacts — is slow, error-prone, and inconsistent between examiners. During active incidents, delayed timeline reconstruction directly extends attacker dwell time. Analysts need repeatable automation that produces a defensible, super-timeline quickly and consistently.

**What you'll build**
Build automation around Plaso/log2timeline to ingest disk images and artifact sources, generate a normalized super-timeline, then apply scripting to filter, enrich, and de-noise events, tagging them to investigative phases and ATT&CK techniques. Add anomaly highlighting (rare processes, off-hours activity, timestamp anomalies indicating timestomping) and export analyst-ready views to Timesketch.

**Objective** — Automate defensible super-timeline generation, enrichment, and analysis to accelerate incident reconstruction.

**Expected outcome** — A scripting framework that turns raw images/artifacts into an enriched, filtered, ATT&CK-tagged timeline exported to Timesketch or CSV.

**Difficulty** — Intermediate — strong tooling (Plaso/Timesketch) exists; skill lies in enrichment logic, filtering, and anomaly heuristics.

**Primary category** — DFIR; Secondary = Incident Response, Threat Hunting

**Security domains** — Primary = Digital Forensics; Secondary = Incident Response, Threat Hunting, Blue Team

**Technical focus** — Timeline Analysis, Super-Timeline Generation, Artifact Parsing, Timestomp Detection, Event Enrichment, Anomaly Highlighting, Log Correlation, Timesketch Integration

**Core technologies** — Languages: Python, Bash; Frameworks/Libraries: Plaso/log2timeline, pandas, dfVFS; Security Tools: Timesketch, RegRipper, Autopsy/Sleuth Kit, KAPE (artifact collection); Cloud/Infra: Docker for Timesketch/Plaso; AI/Data: pandas, optional ML for anomaly scoring; Databases: Elasticsearch (Timesketch backend), SQLite

**Where it's valued** — DFIR consultancies, MSSPs, law enforcement, enterprise SOCs, financial/healthcare incident teams

**Careers it builds toward** — DFIR Analyst, Incident Responder, Threat Hunter, Forensic Examiner, SOC Tier 3

**Framework alignment**
• MITRE ATT&CK: Defense Evasion — Indicator Removal: Timestomp (T1070.006), Clear Windows Event Logs (T1070.001); timeline reconstruction detects Persistence and Execution techniques across the matrix
• NIST CSF 2.0: Detect (DE.AE anomaly detection), Respond (RS.AN — forensics/timeline), Recover (supports RC.RP)
• OWASP: N/A (host/endpoint forensics)
• CIS Controls v8: 8 (Audit Log Management), 17 (Incident Response Management), 13 (Network Monitoring & Defense)

**Innovation** — Improved Existing Solution — Plaso/Timesketch are standard, but automated enrichment, ATT&CK tagging, and anomaly heuristics on top add meaningful analyst leverage. Scores: Innovation 6/10, Startup 5/10, Enterprise-Readiness 6/10, Patent = No.