AI-Powered Anomaly Detection: Zero-Day Threat Protection

Traditional rule-based threat detection can only stop known attack patterns. G8KEPR's AI-powered anomaly detection learns your API's normal behavior and automatically detects zero-day threats, account takeovers, and sophisticated attacks that bypass traditional WAF rules.

🧠 How It Works

Learning Phase (7-14 days)

ML models analyze request patterns, user behavior, and API usage to build a baseline

Detection Phase (Ongoing)

Real-time scoring detects anomalies and flags suspicious behavior before damage occurs

What It Detects

1. Account Takeover (ATO) Attacks

Detects when a compromised account shows unusual behavior patterns:

Example indicators:

• Login from new country (user normally in US, suddenly accessing from Nigeria)
• API calls at unusual times (user normally 9am-5pm PST, suddenly 3am)
• Endpoints never used before (user typically calls /profile, suddenly /admin/users)
• Spike in request volume (normal 20 req/min, suddenly 500 req/min)

Anomaly Score: 94/100 → Action: Require MFA step-up or block + alert SOC

2. Zero-Day API Exploits

Catches novel attack patterns that don't match known signatures:

POST /api/user/update
{
  "userId": "123",
  "name": "John Doe",
  "__proto__": {
    "isAdmin": true
  }
}

🚨 Anomaly: Prototype pollution attempt
   Reason: "__proto__" key never seen in 10M+ training samples
   Confidence: 98.7%
   Action: Block + Log + Alert

3. Scraping & Data Exfiltration

Identifies coordinated scraping attacks across distributed IPs:

Pattern detected:

• 47 different IPs
• All using similar User-Agents (Chrome 120.x on Windows)
• Sequentially requesting /users/1, /users/2, /users/3...
• Each IP staying just below rate limit (99 req/min, limit is 100)

Verdict: Distributed scraping campaign → Block all 47 IPs + similar fingerprints

ML Models & Training

G8KEPR uses multiple specialized models for different threat categories:

Model	Algorithm	Training Data	Accuracy
User Behavior	Isolation Forest	Per-user request patterns	97.2%
Payload Analysis	Transformer (BERT)	10M+ API requests	95.8%
Traffic Pattern	LSTM Autoencoder	Time-series request data	93.4%
IP Reputation	Random Forest	Historical attack data	98.1%

Training Pipeline

Data Collection (Continuous)

Every request logged: headers, payload, response time, user context

Feature Engineering (Hourly)

Extract 200+ features: request frequency, payload entropy, timing patterns

Model Retraining (Daily)

Models updated nightly with last 30 days of data + feedback loop corrections

A/B Testing & Deployment (Weekly)

New models shadow-tested before rollout to avoid false positive spikes

Dashboard & Explainability

Unlike black-box ML systems, G8KEPR explains why each anomaly was flagged:

Request ID: req_8x4k2mp9

User: user_42 | IP: 203.0.113.45 | Time: 2025-07-15 14:23:18 UTC

BLOCKED

POST /api/transfer → {"amount": "50000", "to": "acc_999"}

Anomaly Score92/100

Top Contributing Factors:

1. Transfer amount 50x user's average ($1,000)+35 pts

2. Recipient account never interacted with before+28 pts

3. Login from new device 4 minutes ago+18 pts

4. IP geolocation: Vietnam (user normally US)+11 pts

Pricing & ROI

Pricing

Add-on Cost:$149/mo

Requires:Growth plan or higher

Training Period:7-14 days

False Positive Rate:<2%

ROI Calculation

Avg cost of ATO incident:$4,200

ATOs prevented/month:~3.5

Monthly savings:$14,700

ROI:9,766%

Q3 2025 Release

AI Anomaly Detection will be available as a $149/mo add-on for Growth and Enterprise plans. Early access beta program starts June 2025 - apply here.