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Feature Extraction System

The feature extraction system is a core component of Superline Agent Detection that collects and processes data from browser sessions to identify patterns that distinguish between human users and AI agents.

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Feature Extraction Architecture

The library uses a modular extraction system with multiple specialized extractors that each focus on different aspects of user behavior.

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Default Extractors

Superline Agent Detection comes with five built-in extractors:

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Feature Processing

Raw features collected by extractors undergo several processing steps:

1

Feature Collection

Extractors collect raw data from user interactions and browser environment

2

Statistical Aggregation

Raw events are aggregated into statistical features (e.g., mean, variance, distribution metrics)

3

Normalization

Features are normalized to comparable scales, often using standardization (z-scores) or min-max scaling

4

Transformation

Some features undergo non-linear transformations to improve their predictive power

5

Feature Selection

Only the most discriminative features are selected for the final model

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Custom Extractors

Documentation for creating custom feature extractors is currently under development.

However, the library supports extending its capabilities by creating your own extractor classes. These classes should inherit from a base FeatureExtractor class (details forthcoming) and implement the required methods for initialization, data collection, and feature computation.

You can then pass your custom extractor constructors in the extractorClasses array within the AgentDetector.init() options. Providing this array will replace the default set of extractors.

// Conceptual example
import { DEFAULT_EXTRACTORS } from '@superline-ai/agent-detection';
import MyCustomExtractor from './my-custom-extractor';

AgentDetector.init({
  // To add to defaults:
  // extractorClasses: [...DEFAULT_EXTRACTORS, MyCustomExtractor],
  
  // To replace defaults:
  extractorClasses: [MyCustomExtractor]
});

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Features Table

The following table shows some of the key features used by the detection model:

Feature CategoryExample FeaturesDescription
Browser MetadataUser agent, Screen dimensionsIdentify unusual browser configurations
Mouse MovementsMovement speed, CurvatureAnalyze natural vs. artificial movements
Keyboard PatternsTyping rhythm, Key press durationDetect human typing patterns
Scroll BehaviorScroll speed, Pause patternsIdentify content consumption patterns
Click PatternsClick precision, Click timingDetect natural click behavior

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Privacy Considerations

The feature extraction system is designed with privacy in mind:

  • No personal information is collected or stored
  • Raw data stays local to the browser
  • Only aggregated statistical features are used for detection
  • No cross-site tracking or fingerprinting for tracking purposes

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Next Steps

Detection Model

Learn how extracted features are processed by the detection model

Custom Extractors

Learn how to create and use custom extractors