Interface Activity Source Detection via Regression Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods to prevent malicious spam bots from accessing web content require human intervention and are not fully automated, which is inefficient and prone to errors.
Innovation Solution
A method that selects input devices interacting with a user interface, generates feature vectors based on received measurements, and uses a pre-defined regression model to determine the source of activity, distinguishing between human and bot interactions without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated detection methods are implemented to distinguish human from bot interactions, then productivity and efficiency are improved, but device complexity increases due to the need for multiple input devices and regression models
Solution Approach 1:
The system segments the detection task by selecting specific input devices from a plurality of available devices based on device characteristics. Different input devices (keyboard, mouse, touchscreen, camera, microphone) are evaluated independently, and measurements from selected devices are processed separately before being combined for final classification. This segmentation allows the system to manage complexity by handling device evaluation in discrete, manageable steps.
Solution Approach 2:
The system transitions from analyzing single-device measurements to multi-dimensional analysis by incorporating measurements from multiple input device types. The regression model processes feature vectors that combine data from different device dimensions (keystroke dynamics, mouse movements, touchscreen pressure, camera images, audio signals), enabling more accurate human-bot differentiation through dimensional expansion.
2Measurement precision
If multiple input device measurements are collected and processed through regression models, then measurement precision for distinguishing human and bot interactions is improved, but loss of time increases due to the complexity of data collection and processing
Solution Approach 1:
The system performs preliminary actions by pre-defining the regression model and establishing device selection criteria before actual interaction detection occurs. Device characteristics are pre-analyzed to determine which devices are most suitable for detection, and the regression model is prepared in advance with appropriate parameters. This preliminary setup reduces processing time during actual detection operations.
Solution Approach 2:
The system applies partial action by selecting only the most relevant input devices from the plurality of available devices based on device characteristics and measurement quality. Rather than processing data from all possible devices, the system identifies and processes measurements from the most informative subset, achieving high detection accuracy while minimizing processing time and computational resources.
3Reliability
If a pre-defined regression model is used to determine the source of activity, then reliability of detection is improved, but device complexity increases due to the need for model implementation and maintenance
Solution Approach 1:
The regression model operates autonomously by automatically processing feature vectors from selected input devices and generating classification results without requiring manual intervention. The system self-manages the detection process by applying the pre-defined model parameters to incoming measurement data, enabling reliable automated differentiation between human and bot interactions while reducing operational complexity.
Solution Approach 2:
The system manages model complexity through parameter changes by adjusting the regression model parameters based on device characteristics and measurement quality. The model parameters are optimized to work effectively with the selected input devices, allowing the system to maintain high reliability while adapting to different device configurations and reducing the burden of model maintenance.
Data Source
AI summary
Methods, systems, computer program products for determining the source of activity during interaction with a user interface are provided. The method comprises selecting one or more input devices from a plurality of available input devices coupled to the user interface and receiving respective measurements for the selected one or more input devices. Based on the received respective measurements, respective feature vectors for the one or more input devices are generated and then inputted to a pre-defined regression model. Then, the source of activity is determined based on a result received from the regression model.