Bot Detection via Unified and Difference Embeddings
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Solution Overview
Problem
Existing methods for detecting computer bots in computer-based interactions, such as CAPTCHA tests, are inadequate as sophisticated bots can overcome these security measures.
Innovation Solution
A prediction system that uses time-series data from user sessions to generate predictions about whether a computer-based interaction is performed by a computer bot. This system converts time-series data into payload embeddings, masks certain embeddings to simulate missing data, and then uses a transformer model to encode and aggregate these embeddings into unified and difference embeddings, which are used to make predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CAPTCHA tests are used to detect computer bots, then implementation simplicity is maintained, but detection accuracy deteriorates as sophisticated bots can overcome these security measures
Solution Approach 1:
The patent segments the bot detection problem into multiple independent analysis components: device fingerprinting (analyzing device characteristics), behavioral analysis (examining interaction patterns), and payload analysis (inspecting data content). Each component operates independently and contributes to the overall detection decision, allowing the system to achieve high accuracy without requiring a single overly complex detection mechanism
Solution Approach 2:
The patent creates a multi-functional detection system that simultaneously performs device identification, behavioral pattern recognition, and security threat assessment. The prediction system integrates multiple detection functions into a unified framework that can handle various types of bot detection tasks using the same core architecture, thereby managing complexity while maintaining versatility
2Reliability
If sophisticated detection methods are implemented to improve bot detection accuracy, then detection reliability improves, but system complexity and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by collecting and analyzing device fingerprints, behavioral patterns, and interaction data before making detection decisions. The system pre-processes incoming interactions by extracting relevant features and comparing them against known bot patterns, enabling reliable detection without requiring overly complex real-time analysis
Solution Approach 2:
The patent introduces intermediary components including prediction systems that act as mediators between raw interaction data and detection decisions, and payload systems that serve as intermediaries for analyzing interaction content. These intermediary layers simplify the overall system architecture by breaking down complex detection tasks into manageable stages, each handled by a specialized component
Data Source
AI summary
Techniques are disclosed that relate to predicting whether a computer-based interaction is being performed by a computer bot. A computer system may receive information describing exhibited user-presence indicators of different types that are associated with the computer-based interaction, including user-presence indicators indicative of whether the computer-based interaction is being performed by a computer bot. The computer system performs a first embedding operation to create a unified embedding that unifies the exhibited user-presence indicators into a single embedding that is representative of an aggregation of the exhibited user-presence indicators. The computer system performs a second embedding operation to create a difference embedding that is representative of a set of differences between expected user-presence indicators for the computer-based interaction and the exhibited user-presence indicators. Based on the unified embedding and the difference embedding, the computer system generates a prediction on whether the computer-based interaction is being performed by a computer bot.