Anomaly Detection in Virtual Environments via Confidence Scoring
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Solution Overview
Problem
Existing systems are unable to efficiently detect network attacks in real-time, allowing unauthorized access and malicious activities to occur before they are detected.
Innovation Solution
A system and method for anomaly detection in virtual environments, which monitors user activities, extracts features, and determines a confidence score to identify and prevent unauthorized access and malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing security systems are used, then they can detect attacks after occurrence, but they cannot detect attacks in real-time before unauthorized access happens
Solution Approach 1:
The system performs preliminary actions by establishing baseline user behavior profiles before attacks occur. It continuously monitors and analyzes normal user activities, interaction patterns, and device characteristics in advance, enabling real-time anomaly detection when deviations from established baselines are detected, thus preventing unauthorized access before it completes
2Measurement precision
If comprehensive user activity monitoring is implemented, then anomaly detection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the monitoring process into distinct functional modules: behavior baseline establishment, real-time activity tracking, anomaly analysis, and confidence scoring. Each module handles specific aspects of user monitoring independently, allowing comprehensive surveillance while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system introduces an intermediary verification device that acts as a mediator between user activities and security decisions. This intermediary analyzes user behavior patterns, compares them against established baselines, and generates confidence scores, thereby improving detection accuracy while centralizing complexity in a dedicated analysis layer
3Object-affected harmful factors
If real-time anomaly detection is implemented, then unauthorized access can be prevented, but additional verification steps increase processing time
Solution Approach 1:
The system applies partial verification by generating confidence scores rather than requiring complete verification for every interaction. When confidence scores indicate low anomaly probability, the system allows transactions to proceed with minimal verification, while only triggering additional verification steps when anomaly thresholds are exceeded, thus preventing malicious activities without universally increasing processing time
Data Source
AI summary
A system for implementing anomaly detection accesses user activities associated with an avatar in a virtual environment. The system extracts features from the user activities, where the features provide information about interactions of the avatar with other avatars and entities in the virtual environment. The system determines a deviation range for each feature, where the deviation range indicates a deviation between the features among the avatars over a certain period. The system determines whether the deviation range for a feature is more than a threshold deviation. If it is determined that a deviation range of a feature is more than the threshold deviation, a confidence score associated with the user is updated based on the deviation range of the feature. If the confidence score is more than a threshold score, the user is not associated with an anomaly. Otherwise, the user is determined to be associated with an anomaly.