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Anomaly Detection

Coming Soon

Anomaly detection is currently in development and will be available soon. This feature will provide advanced AI-powered threat detection capabilities.

Our models continuously retrain every few minutes using a combination of your logs and our threat intelligence system. This allows us to adapt in near real time and provide the most accurate possible responses.

Overview​

Anomaly detection will provide:

  • Real-time adaptation - Models retrain every few minutes
  • Behavioral analysis - Learn normal patterns from your traffic
  • Threat intelligence integration - Combine with global threat data
  • Automatic response - Adapt security rules based on detected anomalies
  • Reduced false positives - Learn from your specific environment

How It Works​

Continuous Learning​

  1. Data Collection - Gather traffic patterns and security events
  2. Model Training - Retrain AI models every few minutes
  3. Pattern Recognition - Identify normal vs. anomalous behavior
  4. Threat Correlation - Cross-reference with threat intelligence
  5. Response Adaptation - Adjust security rules automatically

Learning Sources​

Our anomaly detection learns from multiple data sources:

  • Traffic patterns - Request frequency, timing, and volume
  • User behavior - Login patterns, navigation, and actions
  • Geographic data - Location-based access patterns
  • Device fingerprints - Browser, OS, and device characteristics
  • Threat intelligence - Global threat feeds and indicators

Detection Capabilities​

Behavioral Anomalies​

  • Unusual access patterns - Off-hours or unexpected locations
  • Volume spikes - Sudden increases in traffic or requests
  • User agent changes - Suspicious browser or device switches
  • Navigation patterns - Unusual page access sequences
  • API usage - Abnormal API call patterns

Security Anomalies​

  • Attack patterns - Emerging threat techniques
  • Credential abuse - Unusual login attempts or patterns
  • Data exfiltration - Suspicious data access patterns
  • Lateral movement - Unusual internal network access
  • Privilege escalation - Suspicious permission changes

Network Anomalies​

  • Traffic anomalies - Unusual network flow patterns
  • Protocol violations - Non-standard protocol usage
  • Geographic anomalies - Impossible travel patterns
  • Time-based patterns - Unusual timing of activities
  • Resource usage - Abnormal system resource consumption

Model Architecture​

Machine Learning Models​

  • LSTM Networks - Long-term pattern recognition
  • Autoencoders - Unsupervised anomaly detection
  • Isolation Forests - Outlier detection algorithms
  • One-Class SVM - Novelty detection
  • Ensemble Methods - Combined model predictions

Feature Engineering​

  • Temporal features - Time-based patterns and trends
  • Statistical features - Mean, variance, and distribution metrics
  • Frequency features - Request rates and intervals
  • Categorical features - User types, locations, and devices
  • Sequential features - Order and sequence patterns

API Integration​

Alert Configuration​

{
  "alerts": {
    "email_notifications": true,
    "webhook_url": "https://your-system.com/webhook",
    "severity_levels": ["high", "critical"],
    "alert_frequency": "immediate",
    "suppression_rules": {
      "time_window": "1h",
      "max_alerts": 10
    }
  }
}

Response Actions​

Automatic Responses​

  • Log - Record anomaly for analysis
  • Alert - Send notification to security team
  • Block - Temporarily block suspicious traffic
  • Challenge - Present additional verification
  • Quarantine - Isolate suspicious users or devices

Manual Responses​

  • Investigation - Detailed analysis of anomaly
  • Rule creation - Generate new security rules
  • Model adjustment - Fine-tune detection sensitivity
  • False positive feedback - Improve model accuracy

Benefits​

Improved Security​

  • Proactive detection - Identify threats before they cause damage
  • Adaptive defense - Security rules evolve with new threats
  • Reduced false positives - Learn from your specific environment
  • Faster response - Automatic threat mitigation

Operational Efficiency​

  • Reduced manual work - Automated threat detection and response
  • Better insights - Understand your traffic patterns and threats
  • Continuous improvement - Models get better over time
  • Cost savings - Reduce security incident response costs

Roadmap​

Phase 1: Core Detection (Q2 2025)​

  • Basic anomaly detection models
  • Traffic pattern analysis
  • Simple behavioral detection
  • API endpoints for analysis

Phase 2: Advanced Features (Q3 2025)​

  • Real-time model retraining
  • Threat intelligence integration
  • Advanced behavioral analysis
  • Automatic response actions

Phase 3: Full Integration (Q4 2025)​

  • Complete API suite
  • Dashboard and visualization
  • Custom model training
  • Enterprise features

Getting Ready​

While anomaly detection is in development, you can prepare by:

  • Enable logging - Ensure comprehensive traffic logging
  • Review current patterns - Understand your normal traffic
  • Plan integration - Design how to incorporate anomaly detection
  • Stay updated - Follow our development progress

Support​

For questions about anomaly detection or to express interest:

  • Documentation - Check back for updates
  • Beta Program - Contact us for early access
  • Feedback - Share your requirements and use cases
  • Updates - Subscribe to our development updates