Edge Computing Security Strategy Selection via Quantitative Risk Assessment

The patent transforms the security strategy selection problem into a parameter optimization problem by quantifying security risks and strategy effectiveness using numerical parameters. The AHP method converts qualitative security assessments into quantitative parameters, enabling systematic comparison and selection of security strategies based on calculated risk scores rather than complex manual evaluation of multiple strategies.

The patent implements an automated security strategy selection system that autonomously evaluates security risks and selects appropriate strategies without requiring manual intervention. The system uses AHP-based quantitative assessment and machine learning algorithms to automatically determine the most effective security strategies for different terminals and data, reducing the operational complexity for users while maintaining high security performance.

The patent pre-calculates security risk parameters and strategy effectiveness scores using AHP methodology before actual security incidents occur. By establishing the evaluation framework and quantifying security parameters in advance, the system reduces the computational burden during real-time security decision-making, as the complex AHP calculations are performed beforehand rather than during critical security events.

The patent implements a tiered security assessment approach that applies full AHP-based quantitative analysis only when necessary, rather than continuously evaluating all security parameters. The system uses machine learning to identify when simplified assessment methods are sufficient, reserving the more computationally intensive AHP calculations for high-risk scenarios, thus optimizing the balance between security performance and computational resource consumption.