Neural Network Weights for Self-Healing Data Protection
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Solution Overview
Problem
Current cybersecurity approaches focus on external defenses around data, lacking internal mechanisms for data to self-aware, self-heal, and self-defend against unauthorized access, modifications, or deletions.
Innovation Solution
Implementing a system where data is transformed into weights for a neural network, allowing a cybernetic engram to reproduce and verify data blocks, using key inputs to trigger operations, and storing weights on a distributed ledger for self-protection and recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is protected using external layers of protection, then data can be defended from unauthorized access, but the data lacks self-awareness and self-healing capabilities
Solution Approach 1:
The patent applies self-service by enabling data to protect itself through embedded neural networks that automatically detect threats, verify authenticity, and execute recovery operations without external intervention. The neural network within the data structure monitors its own integrity and autonomously responds to security incidents, transforming data from a passive object into an active protective entity.
Solution Approach 2:
The patent implements nesting by embedding a neural network directly within the data structure itself. The neural network is contained within the data block, allowing it to perform security functions internally. This nested architecture enables the data to contain its own protective mechanisms, creating a self-contained security system that operates independently of external protection layers.
2Extent of automation
If data is transformed into neural network weights, then self-awareness and self-healing are enabled, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the security system into discrete functional modules: neural network weights for authentication, verification networks for integrity checking, and recovery networks for self-healing operations. Each module performs a specific security function and can operate independently, making the overall complex system manageable through modular architecture.
Solution Approach 2:
The patent implements universality by designing the neural network to perform multiple security functions simultaneously - authentication, verification, and recovery operations. The same neural network structure can be configured to execute different operations based on input commands, eliminating the need for separate dedicated systems for each security function and thereby reducing overall complexity.
3Reliability
If data blocks are replaced with neural network weights, then self-defending capability is achieved, but data accessibility and operations may be affected
Solution Approach 1:
The patent applies the intermediary principle by introducing command networks as mediators between external systems and the neural network weights. These command networks translate user operations into commands that the neural network can execute, and vice versa. This intermediary layer maintains smooth data accessibility and operation while the underlying data is represented as neural network weights, preventing direct conflicts between accessibility needs and self-defending capabilities.
Data Source
AI summary
Various embodiments include methods and devices for transforming a data block into weights for a neural network. Some embodiments may include training a first neural network of a cybernetic engram to reproduce the data block, and replacing the data block in memory with weights used by the first neural network to reproduce the data block.