Virtual Environment Anomaly Detection Using User Clustering
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Solution Overview
Problem
Existing systems fail to efficiently detect network attacks and unauthorized access in virtual environments, allowing bad actors to gain unauthorized access and perform malicious activities.
Innovation Solution
A system and method for anomaly detection in virtual environments using user clustering, outlier detection, and historical data transfer paths, which monitors user activities to determine confidence scores and authorize or deny interactions based on these scores, preventing malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems monitor user activities in virtual environments, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments user activity monitoring into multiple independent modules: user clustering module, outlier detection module, and confidence score calculation module. Each module processes specific aspects of user behavior independently, then integrates results to produce comprehensive anomaly detection. This segmentation reduces overall system complexity by making each component more manageable and easier to implement.
Solution Approach 2:
The patent introduces a confidence score as an intermediary mechanism that bridges raw user activity data and final anomaly detection decisions. The confidence score aggregates information from multiple sources (clustering results, outlier detection, historical data) into a single interpretable metric, simplifying the decision-making process and reducing the complexity of implementing comprehensive security protocols.
2Reliability
If the system monitors user activities to detect anomalies, then security protection is improved, but processing time increases
Solution Approach 1:
The system performs preliminary clustering of users into groups based on behavioral patterns before actual anomaly detection is needed. By pre-establishing user profiles and activity baselines, the system eliminates the need for time-consuming real-time analysis during actual security events, significantly reducing processing time while maintaining high security protection.
Solution Approach 2:
The patent applies partial action by focusing monitoring resources on high-risk activities and user clusters rather than uniformly analyzing all user actions. The outlier detection mechanism identifies and prioritizes only the most suspicious behaviors for detailed investigation, reducing overall processing time while maintaining comprehensive security coverage through targeted monitoring.
3Measurement precision
If the system uses multiple detection methods (clustering, outlier detection, historical data), then detection accuracy is improved, but computational resources increase
Solution Approach 1:
The system applies local quality by tailoring detection methods to specific user clusters and activity types rather than applying uniform computational resources across all users. Different clustering algorithms and analysis depth are applied locally to different user groups based on their risk profiles and behavioral patterns, improving detection accuracy for high-risk scenarios while reducing overall computational resource consumption.
Solution Approach 2:
The patent dynamically adjusts computational parameters such as clustering granularity, outlier detection sensitivity, and historical data retention periods based on system state and detected anomalies. By changing these parameters adaptively, the system maintains high detection accuracy when needed while minimizing computational resource consumption during normal operation, optimizing the balance between accuracy and resource usage.
Data Source
AI summary
A system for optimizing anomaly detection determines, based on a confidence score, user clustering information that indicates a cluster to which a user belongs, such that if the confidence score is more than a threshold score, the user clustering information indicates that the user belongs to a first cluster. Otherwise, the user clustering information indicates that the user belongs to a second cluster. The system determines, based on user activities in a virtual environment, user outlier information that indicates whether the user is associated with an unexpected activity. The system determines virtual resource routing information that comprises routings of virtual resources between the avatar and the other avatars within the virtual environment. The system updates the confidence score based at least in part upon at least one of the user clustering information, the user outlier information, or the virtual resource routing information.