Machine Learning Model for Predicting System Recovery Scenarios
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Solution Overview
Problem
Current security solutions focus on preventing recovery scenarios by detecting and patching system vulnerabilities, but they lack the ability to predict and mitigate emerging recovery scenarios effectively, often resulting in significant damage and inefficiencies in system recovery processes.
Innovation Solution
A method and security system that utilize machine learning to obtain system recovery indicators and predict the likelihood of a recovery scenario, allowing for preemptive actions to minimize consequences and enhance recovery outcomes by using a model trained on system recovery indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to predict recovery scenarios, then system security and recovery effectiveness are improved, but device complexity and computational resource requirements increase
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical recovery data beforehand. The models are pre-trained to recognize patterns and predict upcoming recovery scenarios, allowing the system to prepare predictive capabilities in advance rather than analyzing data in real-time during crises, thus improving reliability while managing complexity through advance preparation
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw system data and recovery predictions. These models act as mediators that process complex patterns in system indicators and translate them into actionable predictions, reducing the complexity burden on the overall system while maintaining high reliability through sophisticated pattern recognition
2Reliability
If regular backups and manual restoration processes are used, then system recovery capability is maintained, but loss of time and operational downtime increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring system indicators and predicting recovery scenarios before they fully manifest. By detecting early signs of system degradation or attack patterns, the system can initiate recovery processes proactively, significantly reducing the time required for full system restoration compared to reactive backup approaches
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning model continuously analyzes system state changes and adjusts predictions in real-time. This feedback loop allows the system to respond dynamically to emerging threats, optimizing recovery timing and reducing overall recovery time by acting on real-time system state information rather than relying on scheduled backups
3Measurement precision
If comprehensive system monitoring and analysis are implemented, then detection precision of recovery scenarios is improved, but use of energy and computational resources increases
Solution Approach 1:
The system applies local quality by focusing computational resources on analyzing specific critical system indicators that are most predictive of recovery scenarios. Rather than uniformly monitoring all system parameters with equal intensity, the machine learning model identifies and prioritizes key indicators, achieving high detection precision while minimizing overall computational energy consumption by concentrating analysis where it matters most
Data Source
AI summary
A method (200) for use in securing a computing system (416) against a recovery scenario from which the computing system would require recovery. The method comprises: i) obtaining (202) system recovery indicators for the computing system; and ii) predicting (204) a likelihood that the computing system will undergo the recovery scenario from the system recovery indicators using a model trained using a machine learning process that takes as input the system recovery indicators.