Anomaly Log Hunter

Board-ready Kinetic Gain surface for parsing access-log anomalies, burst abuse, and suspicious source patterns before they distort traffic and incident signals.

• Live: http://anomaly.kineticgain.com/
• Repo: https://github.com/mizcausevic-dev/anomaly-log-hunter

Why this exists

Raw logs are usually where suspicious traffic goes to disappear. By then:

• scrapers have already harvested commercial pages
• credential stuffing looks like ordinary auth noise
• synthetic referrals seed attribution with fake demand
• incident response gets stuck arguing about whether a spike was real, malicious, or just marketing

anomaly-log-hunter turns abnormal traffic patterns into a business-readable queue before they distort resilience and growth decisions.

What it includes

• TypeScript control plane for suspicious traffic, burst abuse, and source-pattern drift
• synthetic anomaly lane covering scraper bursts, auth noise, and attribution-shaped traffic
• reusable outputs for impact, block coverage, source clustering, and investigation posture
• prerendered static site, JSON payloads, screenshots, and docs

Product depth

Anomaly Log Hunter is a traffic-integrity proof surface for the moment where Security, Growth, and Platform teams need the same answer: is this traffic real, risky, expensive, or misleading?

• SaaS GTM analyst view: protects campaign and funnel reporting from scraper loops, synthetic referrals, and bot-shaped demand before budget decisions are made from polluted signals.
• SaaS value architect view: translates abnormal request behavior into estimated commercial exposure, blocked coverage, and route-level priority so containment reads as recoverable margin.
• Technical buyer view: keeps fingerprint, source, ASN, burst rate, route pattern, severity, and next action attached to each anomaly instead of leaving raw logs as isolated ops evidence.
• Executive narrative: shows whether leadership should block, monitor, escalate, or explain the abnormal traffic before it becomes false incident noise or bad growth data.

What these repos have in common

This repo follows the broader Kinetic Gain pattern: turn operational exhaust into decision evidence. Each surface models a messy control lane, names the risk in buyer-readable language, attaches an owner or next action, exposes reusable JSON, and ships a static proof page that can be reviewed without internal system access.

Operating workflow

1. Ingest sampled access-log records, source fingerprints, route patterns, and block outcomes.
2. Classify each burst by abuse category, severity, estimated commercial impact, and containment posture.
3. Group repeated source behavior so teams can distinguish isolated noise from coordinated abuse.
4. Publish an executive-readable proof page plus JSON outputs that Security, Growth, and Platform teams can review together.

Routes

• /
• /anomaly-lane
• /source-patterns
• /verification
• /docs

API

• /api/dashboard/summary
• /api/anomaly-lane
• /api/source-patterns
• /api/verification
• /api/sample

Screenshots

Local Development

cd anomaly-log-hunter
npm install
npm run dev

Open:

• http://127.0.0.1:5360/
• http://127.0.0.1:5360/anomaly-lane
• http://127.0.0.1:5360/source-patterns
• http://127.0.0.1:5360/verification
• http://127.0.0.1:5360/docs

Validation

• npm run verify
• npm run prerender
• npm run render:assets

Docs

• Architecture
• Origin
• Changelog