Virtual Environment Security Analysis via Multi-Layer Event Correlation
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Solution Overview
Problem
Virtual environments, such as metaverses, are vulnerable to security threats, including traditional and new threats specific to these environments, which existing technologies have not adequately addressed.
Innovation Solution
A system that analyzes data from virtual environment layers to detect and correlate events, using machine learning algorithms to predict future security events and determine ranked solutions, incorporating an event machine learning algorithm for event detection and classification, and a solution machine learning algorithm for solution determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security technologies are used in virtual environments, then existing security measures are maintained, but they fail to adequately address new threats specific to virtual environments
Solution Approach 1:
The system performs preliminary actions by detecting and correlating events across multiple virtual environment layers before threats fully materialize. The event correlation engine analyzes patterns in advance, enabling predictive security measures that prevent threats rather than merely responding to them after occurrence.
Solution Approach 2:
The patent introduces a new dimension of analysis by correlating events across multiple layers (infrastructure, runtime, application, data layers) simultaneously. This multi-layer correlation approach transforms traditional single-layer security into a holistic multi-dimensional security framework that captures complex cross-layer threat patterns.
2Reliability
If event correlation across multiple layers is implemented, then predictive security capability is improved, but system complexity increases
Solution Approach 1:
The security system is segmented into distinct functional modules: event detectors for each layer, an event correlation engine, and a predictive analytics component. This segmentation allows each module to specialize in specific tasks, managing complexity through modular architecture while maintaining comprehensive multi-layer correlation capability.
Solution Approach 2:
The event correlation engine serves as an intermediary layer that receives events from multiple virtual environment layers, processes correlations, and generates predictive insights. This intermediary abstraction simplifies the complexity by providing a unified interface between diverse data sources and the predictive analytics system.
3Measurement precision
If comprehensive data analysis is performed to detect and correlate events, then security prediction accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary filtering and preprocessing of events at each layer before correlation analysis. By pre-processing data and identifying potentially relevant events in advance, the system reduces the computational burden during correlation and prediction phases, maintaining accuracy while reducing processing time.
Solution Approach 2:
The event correlation engine focuses on correlating only the most relevant events across layers rather than analyzing every possible event combination. This selective correlation approach achieves sufficient prediction accuracy by concentrating computational resources on high-value correlations while ignoring low-impact events.
Data Source
AI summary
The following relates generally to computer security, and more particularly relates to computer security in a virtual environment, such as a metaverse. In some embodiments, one or more processors: receive a set of known events (e.g., security threats) including event classifications; receive data of layers of the virtual environment; detect events in the data of the layers of the virtual environment; and determine correlations between the events in the data of the layers of the virtual environment. The correlations may be between events in different layers of the virtual environment. The one or more processors may also predict future events by analyzing the detected events.