Webpage Event Feature Transformation for Human-Bot Detection
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Solution Overview
Problem
Existing online form security systems struggle to effectively distinguish between human and bot activities, leading to vulnerabilities and potential security threats.
Innovation Solution
A method and apparatus that collect time-series data on webpage events, derive classifications, perform functional transformations, and train a machine learning model to determine bot and non-bot behavior, utilizing features such as event sequences, timing, and metadata to differentiate between human and bot interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing online form security systems are used, then basic security protection is provided, but the ability to distinguish between human and bot activities is insufficient
Solution Approach 1:
The system transforms raw event data into multiple derived features by applying different mathematical operations (mean, median, mode, standard deviation, variance, min, max) across time windows. This parameter transformation enables the machine learning model to detect subtle behavioral patterns that distinguish human from bot activities, directly improving detection accuracy while reducing security vulnerabilities.
Solution Approach 2:
The patent replaces traditional rule-based security mechanisms with a machine learning-based detection system. The machine learning model processes transformed feature data to make intelligent distinctions between human and bot behaviors, providing more accurate and adaptable security protection compared to static rules.
2Measurement precision
If machine learning models are trained with transformed features, then bot behavior detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments the data processing into distinct stages: collecting raw event data, transforming it into multiple feature variants using mathematical operations, selecting relevant features, and feeding them to the machine learning model. This segmentation makes the complex process manageable and modular, where each stage handles a specific aspect of data preparation.
Solution Approach 2:
The patent performs feature transformation and selection as preliminary actions before machine learning model training. By pre-processing the data to create meaningful features (mean, median, standard deviation, etc.) across different time windows, the system prepares the data in advance, reducing the complexity during actual model training and execution.
Data Source
AI summary
Systems, methods, apparatuses, and computer program products for human or bot activity detection. The method may include, collecting time-series data on one or more events occurring on a webpage. The method may also include deriving classifications of the one or more events. The method may further include performing functional transformations of the time-series data. In addition, the method may include determining potential features of the one or more events based on a combination of the classifications of the one or more events, and results of the functional transformation. Further, the method may include training a machine learning model with the potential features. The method may also include determining, via the machine learning model, bot behavior and non-bot behavior of the one or more events.