Hierarchical Security Model Parameter Updates
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Solution Overview
Problem
As networks grow larger and more complex, existing machine learning models struggle to update efficiently in response to new actions or threats, leading to delays in mitigating security risks and potential exposure of the network to security threats.
Innovation Solution
A hierarchical learning model is deployed across devices, allowing local updates on individual devices, cluster updates when a threshold is met, and global updates for widespread threats, ensuring that updates are proportional to the threat level and conserve system resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional centralized machine learning model is used to evaluate all actions on the network, then comprehensive security coverage is achieved, but the time and energy required for model updates increases significantly
Solution Approach 1:
The centralized machine learning model is segmented into distributed learning models deployed across multiple devices on the network. Each device maintains a local copy of the model and updates its parameters independently based on local data, eliminating the need for centralized model updates and reducing update time while maintaining comprehensive security coverage through coordinated parameter synchronization.
Solution Approach 2:
The system transitions from a single centralized model to a multi-dimensional distributed architecture where model parameters are synchronized across devices through cluster servers and global servers. This dimensional expansion allows parallel processing of security evaluations across the network while maintaining model consistency through hierarchical parameter updates.
2Productivity
If the machine learning model is updated frequently to account for new threats, then security responsiveness improves, but system energy consumption increases
Solution Approach 1:
Each device performs local model updates using its own data and computational resources, rather than requiring centralized model retraining and distribution. This local processing reduces the energy overhead of model updates while maintaining security responsiveness, as devices can immediately adapt to new threats detected in their local environment without waiting for centralized updates.
Solution Approach 2:
The distributed learning models on each device autonomously update their parameters based on local security events and threats, without requiring manual intervention or centralized coordination for every update. This self-service capability enables frequent security updates with minimal energy expenditure, as devices independently manage their own model maintenance.
3Stability of the object's composition
If model updates are propagated to all devices in the network, then consistent security policy is maintained, but network bandwidth and processing resources are wasted on low-threat scenarios
Solution Approach 1:
Instead of propagating model updates to all devices in the network, the system selectively updates only those devices that require them based on local threat detection and parameter change thresholds. This partial action approach maintains security policy consistency for affected devices while avoiding unnecessary resource consumption on devices where updates are not needed, reducing overall network energy waste.
Data Source
AI summary
A system includes devices, first and second cluster servers, and a global server. The first cluster server updates a second set of parameters for the first device and a first set of parameters for the second device. The second cluster server updates a fourth set of parameters for the third device and a third set of parameters for the fourth device. The global server updates the first set of parameters and the second set of parameters for the second cluster server and updates the third set of parameters and the fourth set of parameters for the first cluster server.