Cloud Security Threat Detection for Sparse User Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud security systems face challenges in detecting threats from users with sparse or no cloud activity data, as they lack sufficient historical data to generate accurate user behavior models for effective threat detection.
Innovation Solution
The implementation of a cloud security system that generates generalized user behavior models for user groups with similar cloud usage behaviors, allowing these models to be applied to users with sparse data, enabling threat detection even when reliable models cannot be developed due to insufficient activity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user behavior models are generated for individual users, then threat detection accuracy is improved, but users with sparse or no activity data cannot have reliable models developed
Solution Approach 1:
The patent merges data from multiple users with similar cloud usage behaviors to create generalized user behavior models. By combining activity data from users exhibiting similar patterns (e.g., similar service actions, data access behaviors, or device types), the system generates robust baseline models even when individual user data is sparse. This allows threat detection to function effectively for new users by leveraging aggregated patterns from the broader user population.
Solution Approach 2:
The patent creates universal generalized behavior models that can be applied across multiple user contexts. These models serve multiple functions: they provide baseline behavior for threat detection, adapt to different cloud service types, and work across various user roles. The generalized models are designed to be universally applicable while still capturing relevant behavioral patterns, allowing the system to handle diverse user scenarios with a unified approach.
2Reliability
If cloud security monitoring is implemented for all users, then security coverage is improved, but false positives increase when insufficient historical data is available
Solution Approach 1:
By merging behavior patterns from multiple users into generalized models, the system establishes more statistically robust baselines for normal behavior. This reduces false positives because the generalized models capture broader, more reliable patterns rather than being based on limited individual user data. The aggregation of data across users provides a stronger foundation for distinguishing normal from anomalous behavior.
3Device complexity
If traditional threat detection methods are used without generalized models, then system complexity is reduced, but threat detection capability is lost for users with sparse data
Solution Approach 1:
The generalized user behavior models serve as universal templates that can be applied to any user, including new users with sparse data. This universal approach maintains system simplicity by using a standardized modeling framework while still enabling sophisticated threat detection. The models are designed to be adaptable to different user contexts without requiring complex customizations for each user type.
Data Source
AI summary
A cloud security system and method implements cloud activity threat detection using analysis of cloud usage user behavior. In particular, the cloud security system and method implements threat detection for users, cloud service providers, or tenants (enterprises) of the cloud security system who are new or unknown to the cloud security system and therefore lacking sufficient cloud activity data to generate an accurate behavior model for effective threat detection. In accordance with embodiments of the present invention, the cloud security system and method performs user behavior analysis to generate generalized user behavior models for user groups, where each user group includes users with similar cloud usage behavior. The user behavior models of the user groups are assigned to users with sparse cloud activity data. In this manner, the cloud security system and method of the present invention ensures effective threat detection by using accurate and reliable user behavior models.