Core Concepts
Detection Model
Understanding the AI agent detection model
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.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
Model Components
The detection model consists of the following key components:Model Parameters
- 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
Scoring Algorithm
The model applies the following steps to generate a detection confidence:1
Feature Preprocessing
Raw features are preprocessed through:
- Standardization (converting to z-scores)
- Normalization (scaling to 0-1 range)
- One-hot encoding for categorical variables
2
Linear Combination
The model calculates a weighted sum of all features:
3
Sigmoid Transformation
The weighted sum is transformed through the sigmoid function to produce a probability between 0 and 1:
4
Classification
The probability is compared to the threshold:
- If probability ≥ threshold: Classified as AI agent
- If probability < threshold: Classified as human
Training Process
While the model training happens offline, understanding the process helps you appreciate how the detection works:- Data Collection: Hundreds of thousands of labeled browser sessions from both humans and AI agents
- Feature Engineering: Identifying the most discriminative features for detection
- Model Training: Using standard machine learning techniques to optimize the model
- Validation: Testing the model against new, unseen data to ensure accuracy
- Parameter Tuning: Fine-tuning the model to balance precision and recall
The library is shipped with pre-trained model parameters, so you don’t need to perform any training yourself.
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)