Log-Aggregation Forensics · Hacking Project

Ingest, normalize, and correlate multi-source logs into a unified forensic timeline that reconstructs an incident's sequence and scope.

**The problem**
During incident response, the truth is scattered across dozens of log sources — firewalls, EDR, cloud audit trails, application logs, DNS — each with different formats, timezones, and clock skew. Analysts waste critical hours normalizing and correlating logs by hand, and inconsistent timestamps cause them to miss or misorder the attack sequence. Without a rigorous aggregation-and-correlation methodology, root cause and blast radius stay hidden.

**What you'll build**
Build a forensic log-aggregation pipeline that ingests heterogeneous logs, normalizes them to a common schema (e.g., ECS or OCSF), corrects for timezone and clock skew, and builds a unified, cross-source timeline. Layer detection logic (Sigma rules) and entity correlation (user, host, IP) on top to reconstruct the attack chain, then output an annotated incident timeline with pivot points and IOC extraction.

**Objective** — Ingest, normalize, and correlate multi-source logs into a unified forensic timeline that reconstructs an incident's sequence and scope.

**Expected outcome** — A pipeline plus dashboard that turns raw multi-source logs into a normalized, deduplicated, time-corrected incident timeline with Sigma-based detections and extracted IOCs.

**Difficulty** — Intermediate — data engineering and normalization skills plus detection logic; conceptually approachable but breadth of sources adds complexity.

**Primary category** — DFIR; Secondary = SOC, Threat Hunting

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

**Technical focus** — Log Normalization, Timeline Reconstruction, Timestamp/Clock-Skew Correction, Sigma Detection Engineering, Entity Correlation, IOC Extraction, SIEM Data Modeling

**Core technologies** — Languages: Python, SQL; Frameworks/Libraries: pandas, Sigma (sigmac/pySigma), ECS/OCSF schemas; Security Tools: Elastic Stack/Kibana, Splunk (optional), Chainsaw, Hayabusa (EVTX), Zircolite; Cloud/Infra: CloudTrail, Azure Activity/Entra logs, syslog collectors; AI/Data: pandas, optional NLP log clustering, anomaly detection; Databases: Elasticsearch/OpenSearch, SQLite, ClickHouse (optional)

**Where it's valued** — Every enterprise SOC, MSSPs, financial services, healthcare, government, cloud providers

**Careers it builds toward** — DFIR Analyst, SOC Analyst (Tier 2/3), Detection Engineer, Threat Hunter, Incident Responder

**Framework alignment**
• MITRE ATT&CK: Detection coverage mapped via Sigma; supports investigation of T1070 (Indicator Removal incl. T1070.001 Clear Windows Event Logs), T1562.002 (Disable Windows Event Logging), T1078 (Valid Accounts)
• NIST CSF 2.0: Detect (DE.AE anomalies and events, DE.CM), Respond (RS.AN), Identify (ID.RA)
• OWASP: A09:2021 Security Logging and Monitoring Failures (Web Top 10); API9:2023 Improper Inventory / logging gaps
• CIS Controls v8: 8 (Audit Log Management — core), 13 (Network Monitoring and Defense), 17 (Incident Response Management)

**Innovation** — Improved Existing Solution — SIEMs and timeline tools exist, but an open, OCSF-normalizing, clock-skew-correcting forensic correlation pipeline with automated attack-chain reconstruction has genuine value. Scores: Innovation 6/10, Startup 6/10, Enterprise-Readiness 7/10, Patent = No.