Cold Start User Anomaly Detection via Aggregate Baseline Models
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Solution Overview
Problem
In cloud computing environments, detecting anomalies in user activity for new or 'cold start' users is challenging due to the lack of historical data, leading to high false positive rates with conventional rule-based approaches.
Innovation Solution
A method that uses an anomaly detection system to analyze historical activity data, train an average user baseline behavior model, and apply anomaly detection model parameters to cold start user activity, detecting anomalies by comparing against the baseline model and displaying alerts for deviations exceeding a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based anomaly detection techniques are used for cold start users, then anomaly detection can be performed, but false positive rates increase significantly
Solution Approach 1:
The patent introduces an intermediary approach by using aggregate user behavior models as a mediator between the cold start user's activity and the anomaly detection system. Instead of directly applying rigid rules to individual user actions (which causes false positives), the system compares user activities against a learned aggregate model of normal behavior patterns, thereby reducing false positive rates while maintaining detection capability
Solution Approach 2:
The patent changes the detection parameters from static hard-coded rules to dynamic aggregate behavior models that are learned from historical data. The system transitions from fixed threshold-based detection to statistical models that capture the distribution of normal user behaviors, allowing for more accurate anomaly detection that adapts to different user contexts without generating false positives
2Measurement precision
If conventional anomaly detection models are trained on historical activity data, then accurate baseline behavior can be learned, but the approach becomes infeasible for new users without historical data
Solution Approach 1:
The patent applies universality by creating an aggregate user behavior model that serves multiple functions: it acts as a baseline for existing users, provides a reference for cold start users, and captures general normal behavior patterns across the user population. This universal model enables the system to handle both established and new users with a single approach, eliminating the need for user-specific training data
Solution Approach 2:
The patent uses copying by creating a synthetic baseline behavior profile that replicates the characteristics of normal user behavior without requiring actual historical data from the specific new user. The system copies aggregate behavior patterns from the population to construct a usable baseline for cold start users, allowing anomaly detection to proceed without individual user history
3Device complexity
If hardcoded predefined rules are used for anomaly detection, then simple implementation is achieved, but detection accuracy deteriorates due to high false positive rates
Solution Approach 1:
The patent transforms the detection approach by changing from fixed hard-coded parameters to learned statistical parameters. Instead of using static rules with fixed thresholds, the system learns behavior patterns from historical data and uses statistical distributions to determine anomalies, significantly improving detection accuracy while maintaining reasonable system complexity through automated model training
Data Source
AI summary
A system and method for detecting cold start user activity anomalies in a cloud computing environment comprise an anomaly detection system collecting historical activity data of users of a plurality of endpoint computers arranged in an account. An average user baseline behavior model is trained for the account from the historical activity data of the users arranged in the account. The anomaly detection system applies the average user baseline behavior model to a cold start user activity and detects an anomaly in response to a comparison between the cold start user activity and the average user baseline behavior model. The anomaly detection system displays an alert based on a determination from the comparison that at least one anomaly of the plurality of anomalies is detected by the cold start user activity deviating from the average user baseline behavior model by a predetermined threshold.