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Detection Model

The Superline Agent Detection library uses a logistic regression model to classify browser sessions as either human or AI agent. This page explains how the model works and processes features to generate detection results.

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Model Architecture

Superline Agent Detection uses a logistic regression model, which is:

• Lightweight: The model has minimal computational requirements and can run entirely in the browser
• Interpretable: The model’s decision process can be understood and traced back to specific features
• Efficient: The detection process completes in milliseconds, even on lower-end devices
• Privacy-preserving: All detection happens locally, with no data sent to external servers

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Model Components

The detection model consists of the following key components:

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Model Parameters

// Simplified representation of model parameters
{
  weights: {
    feature1: 0.85,
    feature2: -0.32,
    // ... other feature weights
  },
  bias: -0.75,
}

• Weights: Coefficients that determine the importance of each feature
• Bias: The base value (intercept) before any feature contributions
• Threshold: The cutoff value for classification is hardcoded to 0.5 in the scoring logic

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Scoring Algorithm

The model applies the following steps to generate a detection confidence:

z = bias + (weight1 * feature1) + (weight2 * feature2) + ...

probability = 1 / (1 + e^(-z))

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Training Process

While the model training happens offline, understanding the process helps you appreciate how the detection works:

1. Data Collection: Hundreds of thousands of labeled browser sessions from both humans and AI agents
2. Feature Engineering: Identifying the most discriminative features for detection
3. Model Training: Using standard machine learning techniques to optimize the model
4. Validation: Testing the model against new, unseen data to ensure accuracy
5. Parameter Tuning: Fine-tuning the model to balance precision and recall

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Model Performance

The current model achieves:

• Accuracy: Greater than 95% on validation data
• False Positive Rate: Less than 3% (humans mistakenly identified as agents)
• False Negative Rate: Less than 5% (agents mistakenly identified as humans)

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Model Updates

The detection model is continuously improved based on new data and insights. Library updates may include new model parameters with enhanced detection capabilities. Always use the latest version to benefit from these improvements.

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Next Steps