Time-Series Event Features for Human–Bot Activity Detection
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Solution Overview
Problem
Existing online form security systems struggle to effectively distinguish between human and bot activities, particularly in the context of online forms that are vulnerable to malicious behavior and unauthorized modifications due to increased network connectivity.
Innovation Solution
A method and apparatus that utilize time-series data collection, classification, functional transformation, and machine learning models to differentiate between human and bot behaviors by analyzing event sequences, timing, and metadata, employing machine learning to train models that identify bot behavior based on derived features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 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 segments the detection process into multiple independent modules: time-series data collection, classification derivation, functional transformation, feature determination, and machine learning model inference. Each module processes specific aspects of event data separately before combining results for final bot detection, improving precision while maintaining manageable complexity
Solution Approach 2:
The patent introduces multiple dimensions of analysis beyond simple event detection: temporal dimension (time-series data), categorical dimension (event classifications), transformation dimension (functional transformations), and feature dimension (multiple derived features). This multi-dimensional approach enables accurate bot-human distinction without overwhelming system complexity
2Reliability
If more security measures are implemented to prevent bot activity, then security improves, but false positives increase
Solution Approach 1:
The system dynamically adjusts detection parameters by performing functional transformations on time-series data and deriving multiple features from event classifications. The machine learning model adapts its decision boundaries based on learned patterns from legitimate user behavior, reducing false positives while maintaining high security reliability through continuous parameter optimization
Solution Approach 2:
The machine learning model incorporates feedback loops that continuously learn from detected patterns and adjust classification thresholds. By analyzing the temporal relationships and functional transformations of events, the system refines its detection precision over time, reducing false positives while maintaining reliable bot detection
3Measurement precision
If time-series data collection and machine learning are used, then bot detection capability improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining event classifications and functional transformation rules before actual detection occurs. Time-series data is collected and preliminarily processed into classified events and transformed features, which are then fed into the machine learning model. This pre-processing reduces real-time computational burden while maintaining high differentiation accuracy
Solution Approach 2:
The patent extracts only the essential features from the time-series event data through functional transformations and classification derivation. By taking out and focusing on the most discriminative features (such as temporal patterns, event sequences, and functional relationships) rather than processing all raw data, the system achieves accurate bot behavior differentiation with reduced processing time
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.