Metaverse Security Bots for Decentralized Data Protection
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Solution Overview
Problem
Decentralized networks, such as blockchain networks, face challenges in data security due to the lack of a central entity for administering security, and this is exacerbated in metaverse environments where standardized robust security systems are not yet established.
Innovation Solution
The implementation of security bots and a simulation engine that monitor and record data interactions in metaverse environments, detect suspicious activities, and simulate data interactions to verify their legitimacy before processing, thereby preventing data theft by blocking unauthorized or scam-related transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If decentralized networks operate without a central entity for security administration, then network autonomy and decentralization are maintained, but data security and governance become challenging
Solution Approach 1:
The patent introduces security bots as intermediary entities that operate within the decentralized network to monitor, detect, and respond to suspicious data interactions. These bots serve as automated mediators that provide security governance without requiring a central authority, thus maintaining network autonomy while improving data security through automated surveillance and intervention mechanisms
2Reliability
If security bots monitor and detect suspicious data interactions in real-time, then data security is improved, but system complexity increases
Solution Approach 1:
The security bots are designed to autonomously perform monitoring, detection, verification, and blocking operations without requiring constant human intervention or complex centralized control systems. The bots self-manage their operational workflows, including automatically analyzing data interaction patterns, verifying suspicious activities through simulation, and executing blocking decisions, thereby reducing the operational complexity burden on the overall system
Solution Approach 2:
The security system is divided into independent security bot entities that operate separately but coordinate through the decentralized network. Each bot handles specific monitoring and detection tasks independently, allowing the system to scale without proportionally increasing complexity. The segmentation of security functions into modular bot components enables manageable system architecture despite the enhanced security capabilities
3Reliability
If suspicious data interactions are blocked automatically, then data theft is prevented, but legitimate data interactions may be hindered
Solution Approach 1:
The system performs preliminary verification of suspicious data interactions by simulating them in a test environment before blocking or allowing them to proceed in the main network. This preliminary action stage enables the system to validate whether suspicious interactions are actually malicious or legitimate, reducing the risk of falsely blocking legitimate data exchanges while maintaining strong security against actual threats
Solution Approach 2:
The security bots continuously monitor data interaction outcomes and use this feedback to refine their detection accuracy. By analyzing patterns from blocked and permitted interactions, the system learns to distinguish more effectively between malicious and legitimate activities, thereby reducing false positives that could hinder legitimate data exchanges while maintaining robust security blocking
4Measurement precision
If simulation engine verifies suspicious data interactions before processing, then false blocking is reduced, but processing time increases
Solution Approach 1:
The simulation engine performs verification only on data interactions that are flagged as suspicious by the security bots, rather than verifying all data interactions. This partial action approach focuses computational resources on potentially malicious activities while allowing clearly legitimate interactions to proceed without simulation overhead, thus minimizing the overall time loss while maintaining high verification accuracy for suspicious cases
Data Source
AI summary
A system includes a plurality of computing nodes that form a blockchain network, wherein one or more of the computing nodes is a metaverse computing node configured to generate a mixed reality environment. A processor of at least one computing node is configured to record a plurality of data interactions performed by a user using a metaverse computing node, determine, based on the plurality of data interactions, a data interaction behavior of the user in relation to the data file, detect that a data interaction performed in relation to the data file of the user is not in accordance with the data interaction behavior of the user, determine that the data interaction is a suspicious data interaction, assign an alert level to the suspicious data interaction, determine at least one action to be performed based on the alert level and perform the at least one action.