Hosted Service Monitoring for Automated Initiator Detection
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Solution Overview
Problem
Detecting and preventing abusive behavior, such as automated attacks, spamming, and data scraping, in hosted services is a significant challenge for organizations as existing methods struggle to effectively identify and block malicious activities in real-time.
Innovation Solution
A hosted service monitoring system that analyzes user actions over time, identifies entities with common features, generates anomaly scores using feature statistics, and blocks automated initiators by projecting these scores into vectors, employing unsupervised or supervised scoring models to determine and mitigate abusive behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to detect abusive behavior, then the system can identify some malicious activities, but the detection accuracy is insufficient and cannot effectively distinguish automated initiators from genuine users
Solution Approach 1:
The patent transforms discrete user action features into continuous vector representations through embedding layers, enabling the system to capture subtle patterns in user behavior. This parameter transformation from discrete to continuous space allows for more nuanced detection of automated initiators while maintaining manageable system complexity through standardized vector operations.
Solution Approach 2:
The patent replaces traditional rule-based detection mechanisms with machine learning models that automatically learn patterns from data. The unsupervised and supervised scoring models substitute manual threshold-setting and feature-engineering approaches, improving detection accuracy while the modular architecture keeps system complexity可控.
2Measurement precision
If the system monitors all user actions in detail to improve detection accuracy, then automated initiators can be identified more precisely, but the computational resources and processing time increase significantly
Solution Approach 1:
The patent segments the detection process into distinct stages: feature extraction, embedding generation, unsupervised scoring, and supervised scoring. This segmentation allows computational resources to be distributed across modular components, enabling accurate anomaly detection while managing overall resource consumption through parallel processing capabilities.
Solution Approach 2:
The patent performs preliminary feature extraction and embedding generation before the actual anomaly scoring process. By pre-processing user actions into standardized vector representations, the system reduces the computational burden during real-time detection, as the complex feature engineering is completed in advance rather than during active monitoring.
3Reliability
If the system blocks all detected automated initiators to prevent harm, then service protection is improved, but genuine users may be incorrectly blocked reducing service availability
Solution Approach 1:
The patent implements a feedback mechanism where the supervised scoring model is trained using labeled data from detected anomalies. The system continuously learns from blocked and approved cases, adjusting its detection thresholds and patterns. This feedback loop improves service protection accuracy while reducing false positives that would otherwise block genuine users, thereby maintaining service availability.
4Measurement precision
If the system analyzes multiple features of user actions to improve detection precision, then automated initiators can be identified more accurately, but the complexity of feature extraction and processing increases
Solution Approach 1:
The patent employs a universal embedding layer that processes multiple different feature types (user identifiers, timestamps, action types, parameters) through a single standardized transformation mechanism. This multi-functional approach allows the system to handle diverse user action features uniformly, improving identification accuracy while reducing the complexity of managing separate processing pipelines for each feature type.
Data Source
AI summary
A hosted service monitoring system detects abuse of a hosted service by monitoring user actions over a period of time. The system will identify an entity, which is a subset of the user actions that share one or more features in common. The system will also identify feature statistics to measure how often the features are associated with the user actions in the entity. The system will project the feature statistics to a vector and use the vector to generate an anomaly score for the entity. The system will determine that the entity is associated with an automated initiator if it generates an anomaly score that exceeds a threshold. Upon determining that the entity that is associated with an automated initiator, the system will cause the hosted service to take an action that will block the automated initiator from accessing the hosted service.