Behavior Analysis

GameShield uses advanced behavior analysis to distinguish between human users and automated bots. This approach provides a more effective security layer than traditional CAPTCHA methods alone.

How It Works

The behavior analysis system works by:

1. Collecting Interaction Data: As users interact with the game challenges, GameShield tracks various interaction patterns
2. Analyzing Patterns: The collected data is analyzed using algorithms designed to identify human vs bot behavior
3. Risk Assessment: A risk score is calculated based on the analysis results
4. Verification Decision: The system uses the risk score to make a final verification decision

Key Metrics

GameShield analyzes several key metrics to determine if a user is human:

Movement Smoothness

Human movements typically have natural variations in speed and direction, while bot movements often show unnatural patterns:

• Human Patterns: Gradual acceleration/deceleration, slight imprecisions, natural curves
• Bot Patterns: Perfectly straight lines, constant velocity, unnatural precision

Reaction Time

Humans have typical reaction time ranges that differ from automated systems:

• Human Patterns: Variable reaction times (typically 200-900ms), occasional delays
• Bot Patterns: Too fast (< 100ms) or too consistent reaction times

Interaction Density

The frequency and distribution of interactions can reveal automated behavior:

• Human Patterns: Natural pauses, varying interaction rates, occasional mistakes
• Bot Patterns: Perfectly timed interactions, unnaturally consistent patterns

Pattern Variability

Humans show natural variability in their interaction patterns:

• Human Patterns: Slight inconsistencies, learning patterns, adaptation
• Bot Patterns: Repetitive behaviors, lack of adaptation, perfect consistency

Implementation

The behavior analysis is implemented in the @gameshield/core package through the BehaviorAnalyzer class:

import { BehaviorAnalyzer } from '@gameshield/core';

// Create a new analyzer instance
const analyzer = new BehaviorAnalyzer();

// Start tracking user behavior
analyzer.startTracking();

// Later, analyze the collected data
const result = analyzer.analyze();
console.log('Is human:', result.isHuman);
console.log('Confidence:', result.confidence);

Analysis Result

The analysis result contains:

• isHuman (boolean): Whether the user is likely human
• confidence (number): Confidence level (0-1) of the determination

Advanced Configuration

For advanced use cases, you can configure the behavior analyzer with custom thresholds:

const analyzer = new BehaviorAnalyzer({
  // Custom thresholds
  movementSmoothnessThreshold: 0.7,
  reactionTimeThreshold: 300,
  interactionDensityThreshold: 0.5,
  patternVariabilityThreshold: 0.6
});

Combining with Game Challenges

The behavior analysis works in conjunction with the game challenges to provide a multi-layered verification approach:

1. Game Challenge: Verifies that the user can complete a specific task
2. Behavior Analysis: Verifies that the user's interaction patterns match human behavior

This combination makes GameShield significantly more difficult for bots to bypass compared to traditional CAPTCHAs.

Privacy Considerations

GameShield's behavior analysis is designed with privacy in mind:

• No Personal Data: The system doesn't collect any personally identifiable information
• Local Processing: Behavior analysis happens in the browser
• Minimal Data Transfer: Only the verification result is sent to the server, not the raw interaction data

Effectiveness Against Bot Types

GameShield's behavior analysis is effective against various types of bots:

Bot Type	Effectiveness	Reason
Simple Scripts	Very High	Cannot mimic human interaction patterns
Headless Browsers	High	Difficult to simulate natural mouse movements
AI-based Solvers	Medium-High	Even advanced AI has difficulty perfectly mimicking human behavior
Human Farms	Medium	Can detect unusual patterns even with human operators

Continuous Improvement

The behavior analysis system is continuously improved through:

1. Pattern Learning: The system learns new patterns to better distinguish humans from bots
2. Threshold Adjustments: Thresholds are adjusted based on real-world data
3. New Metrics: Additional metrics are added to improve detection accuracy

Best Practices

To maximize the effectiveness of behavior analysis:

1. Use Appropriate Game Types: Different games reveal different behavior patterns
2. Combine with Server Verification: Always verify tokens server-side
3. Adjust Difficulty Based on Risk: Use higher difficulty for high-risk actions
4. Monitor Verification Rates: Watch for unusual changes in verification success rates

Technical Details

For those interested in the technical implementation, the behavior analyzer:

1. Attaches event listeners to track mouse movements, clicks, and keyboard interactions
2. Samples these events at appropriate intervals to avoid performance issues
3. Calculates statistical measures (variance, standard deviation, etc.) on the collected data
4. Applies machine learning algorithms to classify the behavior as human or bot
5. Generates a confidence score based on the classification results

This approach provides a robust security layer that is difficult for automated systems to bypass while maintaining a good user experience.

Next Steps

• Learn about server-side verification for complete protection
• Explore integration examples for common use cases