PLANNED FEATURE - NOT YET AVAILABLEThis feature is currently in development and planned for release in Q3 2025. The documentation below describes the planned functionality and is subject to change. For current security features, see Rate Limiting, Request Signing, and OAuth2 Security.

AI-Powered Threat Detection

Protect your APIs with intelligent threat detection. KnoxCall’s AI engine analyzes request patterns in real-time to identify suspicious activity, potential attacks, and security anomalies before they cause damage.

What is AI Threat Detection?

Traditional security relies on manual rules and fixed thresholds. AI Threat Detection uses machine learning to:

🧠 Learn normal behavior for your APIs
🚨 Detect anomalies automatically
🎯 Identify attack patterns (SQL injection, XSS, brute force)
🔮 Predict threats before they escalate
📊 Adapt continuously as your traffic evolves

How It Works

1. Baseline Learning

KnoxCall AI learns your normal traffic patterns:

Week 1: Learning Phase
├─ Normal request volume: 1,000-5,000/hour
├─ Common endpoints: /api/users, /api/orders
├─ Typical latency: 50-200ms
├─ Standard error rate: 1-2%
└─ Geographic distribution: 60% US, 30% EU, 10% APAC

2. Anomaly Detection

AI compares real-time traffic against baseline:

Current Activity:
├─ Request volume: 50,000/hour ⚠️ (10x normal)
├─ New endpoint: /api/admin/delete-all ⚠️
├─ Latency: 5,000ms ⚠️ (10x normal)
├─ Error rate: 45% ⚠️ (23x normal)
└─ Location: Russia ⚠️ (never seen before)

AI Assessment: 🚨 HIGH THREAT PROBABILITY

3. Threat Classification

AI categorizes threats by type:

🔴 Critical: Active attack in progress
🟠 High: Suspicious pattern detected
🟡 Medium: Unusual activity
🟢 Low: Minor anomaly

4. Automated Response

Based on threat level, KnoxCall can:

Alert admins via email/Slack/SMS
Rate limit suspicious IPs
Block confirmed attacks
Log detailed forensics
Generate security reports

Detected Threat Types

1. Brute Force Attacks

Pattern:

IP: 192.168.1.100
Request: POST /api/login
Status: 401 Unauthorized
Frequency: 1,000 attempts in 5 minutes

AI Detection:

Rapid successive failures
Same IP, different credentials
Exponential retry pattern

Response:

Block IP after 10 failed attempts
Rate limit: 1 request/minute
Alert security team

2. SQL Injection Attempts

Pattern:

GET /api/users?id=1' OR '1'='1
GET /api/search?q=admin'--
POST /api/data {"name": "test'; DROP TABLE users;--"}

AI Detection:

SQL keywords in parameters
Quote and comment patterns
UNION, DROP, SELECT in unexpected places

Response:

Block request immediately
Log full payload
Alert with OWASP category

3. API Abuse / Scraping

Pattern:

IP: 203.0.113.50
Requests: 10,000 in 10 minutes
Endpoints: /api/products/1, /api/products/2, ... /api/products/10000
User-Agent: python-requests/2.28.0

AI Detection:

Sequential ID enumeration
Suspicious user agent
No typical browsing pattern
Ignores rate limit headers

Response:

Rate limit aggressively
Require CAPTCHA
Block after threshold

4. Credential Stuffing

Pattern:

1000 login attempts with:
- Different usernames
- Different passwords
- Same IP or distributed botnet
- Known leaked credential combinations

AI Detection:

Cross-reference with breach databases
Unusual geographic distribution
Coordinated timing
Low success rate

Response:

Block IP ranges
Require 2FA
Force password reset

5. DDoS Attacks

Pattern:

Sudden traffic spike:
- Normal: 1,000 req/min
- Attack: 100,000 req/min
- From: 10,000 unique IPs
- Target: Single endpoint

AI Detection:

Abnormal traffic volume
Distributed sources
Identical request patterns
Low latency variance (bots)

Response:

Activate DDoS mitigation
Enable challenge response
Rate limit globally
Contact CDN/ISP

6. Account Takeover Attempts

Pattern:

User login from:
- New device
- New location (5,000 miles away)
- Within 5 minutes of previous login
- Different user agent

AI Detection:

Impossible travel
Device fingerprint mismatch
Unusual access patterns
Credential change attempts

Response:

Require additional verification
Lock account temporarily
Alert user via email
Log forensic details

AI Features

Behavioral Analysis

Track individual client behavior:

Client: mobile-app-ios
Normal Behavior:
├─ Active: 9 AM - 9 PM EST
├─ Endpoints: /api/feed, /api/profile, /api/posts
├─ Frequency: 10-50 requests/hour
├─ Locations: US, Canada
└─ Success rate: 98%

Anomaly Detected:
├─ Time: 3 AM EST ⚠️
├─ Endpoint: /api/admin/users ⚠️
├─ Frequency: 500 requests/minute ⚠️
├─ Location: Unknown VPN ⚠️
└─ Success rate: 20% ⚠️

Threat Level: 🔴 CRITICAL

Pattern Recognition

Identify complex attack patterns:

Attack Pattern: Advanced Persistent Threat (APT)
├─ Phase 1: Reconnaissance (scanning endpoints)
├─ Phase 2: Exploitation (SQL injection attempts)
├─ Phase 3: Privilege escalation (admin API calls)
└─ Phase 4: Data exfiltration (bulk downloads)

AI detected all 4 phases and blocked at Phase 2

Predictive Alerts

Get warnings before attacks happen:

🔮 Predictive Alert: Potential Attack
├─ Confidence: 85%
├─ Type: Brute force attack
├─ Target: /api/login
├─ Predicted timing: Next 30 minutes
└─ Recommended action: Enable rate limiting

Configuration

Step 1: Enable AI Threat Detection

Navigate to Security → AI Threat Detection
Toggle Enable AI Detection to ON
Choose sensitivity level:

Low: Only critical threats
Medium: High and critical threats (recommended)
High: All anomalies

Step 2: Configure Response Actions

Set automated responses:

Critical Threats:
  - Block IP immediately
  - Send SMS alert
  - Log to SIEM

High Threats:
  - Rate limit to 10 req/min
  - Send email alert
  - Monitor for 1 hour

Medium Threats:
  - Log and monitor
  - Send daily summary

Low Threats:
  - Log only

Step 3: Define Protected Routes

Choose which routes to protect:

Route: payment-api
AI Protection: Maximum
Response: Block on threat

Route: public-api
AI Protection: Medium
Response: Rate limit on threat

Route: internal-tools
AI Protection: Low
Response: Alert only

Security Dashboard

Threat Overview

Real-time threat monitoring:

🟢 System Status: SECURE
📊 Threats Detected (24h): 47
🚫 Attacks Blocked: 38
⚠️ Active Investigations: 2

Threat Feed

Live feed of detected threats:

[12:45:32] 🔴 CRITICAL: SQL Injection blocked
  IP: 203.0.113.100
  Route: /api/search
  Payload: admin'--
  Action: Blocked

[12:42:15] 🟠 HIGH: Brute force detected
  IP: 198.51.100.50
  Route: /api/login
  Attempts: 50 in 2 minutes
  Action: Rate limited

[12:38:04] 🟡 MEDIUM: Unusual traffic pattern
  Client: mobile-app-android
  Volume: 5x normal
  Action: Monitoring

Threat Map

Geographic visualization of threats:

       🔴 5 attacks
      (Europe)
           │
    🟠 12 threats
    (North America)
           │
       🟡 3 anomalies
       (Asia Pacific)

Integration with External Tools

SIEM Integration

Send threat data to your SIEM:

Splunk
Datadog
Sumo Logic

Configure Splunk HTTP Event Collector
Send threats as JSON events
Create dashboards and alerts

Use Datadog API integration
Send threat events as logs
Create security monitors

Configure HTTP source
Stream threat data
Build security analytics

Webhook Notifications

Send threats to custom endpoints:

POST https://your-security-system.com/threats
{
  "threat_id": "thr_abc123",
  "severity": "critical",
  "type": "sql_injection",
  "ip": "203.0.113.100",
  "route": "/api/search",
  "timestamp": "2025-01-20T15:30:00Z",
  "details": {
    "payload": "admin'--",
    "action_taken": "blocked"
  }
}

Machine Learning Models

KnoxCall uses multiple ML models:

1. Anomaly Detection Model

Algorithm: Isolation Forest Training: Continuous on your traffic Features: 50+ traffic characteristics Accuracy: 95%+ true positive rate

2. Threat Classification Model

Algorithm: Gradient Boosting (XGBoost) Training: 10M+ labeled attack samples Output: Threat type and confidence Accuracy: 98% classification accuracy

3. Pattern Recognition Model

Algorithm: LSTM Neural Network Purpose: Detect multi-stage attacks Window: Analyzes 1-hour sequences Accuracy: 92% attack chain detection

Best Practices

1. Start with Learning Mode

Let AI learn for 1-2 weeks before enforcing:

Week 1-2: Learning mode (alerts only)
Week 3+: Enforcement mode (block threats)

2. Review False Positives

Check alerts weekly:

False Positive Rate: < 1%
Review: Weekly
Action: Whitelist legitimate patterns

3. Tune Sensitivity

Adjust based on your risk tolerance:

High Security: High sensitivity
Balanced: Medium sensitivity (default)
Performance Critical: Low sensitivity

4. Combine with Other Security

AI works best alongside:

✅ Rate limiting
✅ IP whitelisting
✅ Request signing
✅ WAF rules

5. Monitor and Adjust

Review AI performance monthly:

Metrics to track:
- Detection rate
- False positive rate
- Response time
- Blocked threats

Troubleshooting

High false positive rate

Solutions:

Lower sensitivity
Whitelist known-good IPs
Add legitimate patterns to training

Attacks getting through

Solutions:

Increase sensitivity
Enable additional security layers
Update threat intelligence feeds

Performance impact

Solutions:

Enable sampling (analyze 10% of traffic)
Use async threat detection
Upgrade to higher tier

Next Steps

Request Signing

Add cryptographic verification

Rate Limiting

Complement AI with rate limits

Alerts

Configure threat alerts

Analytics

View security analytics

📊 Statistics

Level: advanced
Time: 20 minutes

🏷️ Tags

security, ai, ml, threat-detection, protection

​AI-Powered Threat Detection

​What is AI Threat Detection?

​How It Works

​1. Baseline Learning

​2. Anomaly Detection

​3. Threat Classification

​4. Automated Response

​Detected Threat Types

​1. Brute Force Attacks

​2. SQL Injection Attempts

​3. API Abuse / Scraping

​4. Credential Stuffing

​5. DDoS Attacks

​6. Account Takeover Attempts

​AI Features

​Behavioral Analysis

​Pattern Recognition

​Predictive Alerts

​Configuration

​Step 1: Enable AI Threat Detection

​Step 2: Configure Response Actions

​Step 3: Define Protected Routes

​Security Dashboard

​Threat Overview

​Threat Feed

​Threat Map

​Integration with External Tools

​SIEM Integration

​Webhook Notifications

​Machine Learning Models

​1. Anomaly Detection Model

​2. Threat Classification Model

​3. Pattern Recognition Model

​Best Practices

​1. Start with Learning Mode

​2. Review False Positives

​3. Tune Sensitivity

​4. Combine with Other Security

​5. Monitor and Adjust

​Troubleshooting

​High false positive rate

​Attacks getting through

​Performance impact

​Next Steps

Request Signing

Rate Limiting

Alerts

Analytics

📊 Statistics

🏷️ Tags

AI-Powered Threat Detection

What is AI Threat Detection?

How It Works

1. Baseline Learning

2. Anomaly Detection

3. Threat Classification

4. Automated Response

Detected Threat Types

1. Brute Force Attacks

2. SQL Injection Attempts

3. API Abuse / Scraping

4. Credential Stuffing

5. DDoS Attacks

6. Account Takeover Attempts

AI Features

Behavioral Analysis

Pattern Recognition

Predictive Alerts

Configuration

Step 1: Enable AI Threat Detection

Step 2: Configure Response Actions

Step 3: Define Protected Routes

Security Dashboard

Threat Overview

Threat Feed

Threat Map

Integration with External Tools

SIEM Integration

Webhook Notifications

Machine Learning Models

1. Anomaly Detection Model

2. Threat Classification Model

3. Pattern Recognition Model

Best Practices

1. Start with Learning Mode

2. Review False Positives

3. Tune Sensitivity

4. Combine with Other Security

5. Monitor and Adjust

Troubleshooting

High false positive rate

Attacks getting through

Performance impact

Next Steps