IoT Transaction Data Security Detection With Trapezoidal Fuzzy Sets
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Solution Overview
Problem
The existing security mechanisms in Internet of Things (IoT) data sharing systems, particularly in market transactions, are inadequate in addressing network attacks and data security issues, especially in the context of limited software/hardware resources and the need for secure data sharing in edge computing environments.
Innovation Solution
A method and device for detecting IoT data security in market transactions using trapezoidal fuzzy sets to calculate attack rates, error rates, repetition rates, and miss rates, determining security detection and false detection values through statistical analysis, and evaluating data security based on these fuzzy sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security mechanisms are used in IoT data sharing systems, then system structure is simple and easy to implement, but security reliability is insufficient against network attacks
Solution Approach 1:
The patent segments the security detection process into multiple independent modules: attack detection module, error rate detection module, repetition rate detection module, and miss rate detection module. Each module calculates specific fuzzy sets for different security aspects, allowing the complex security detection task to be divided into manageable components that can be processed independently and then integrated.
Solution Approach 2:
The patent introduces trapezoidal fuzzy sets as an intermediary mechanism between raw security data and security assessment results. The fuzzy sets (NADi, kei, kRi, kLi) serve as mediators that transform discrete attack counts and error rates into continuous membership values, enabling nuanced security evaluation that bridges the gap between simple counting and complex security analysis.
2Measurement precision
If comprehensive security detection is performed on IoT data, then measurement precision of security status is improved, but loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining the trapezoidal fuzzy set structures and membership function relationships before actual security detection. The framework establishes predetermined calculation methods for converting attack counts, error rates, repetition rates, and miss rates into fuzzy membership values, so that during runtime, only data collection and formula substitution are needed, significantly reducing processing time while maintaining comprehensive detection.
Solution Approach 2:
The patent transforms security measurement from discrete integer counts (number of attacks) to continuous fuzzy membership values (0-1 scale). This parameter change allows for more precise security assessment by capturing partial truths and uncertainties, while the mathematical transformations use efficient computational formulas that balance precision with processing speed.
3Measurement precision
If fuzzy set calculations are performed for multiple security metrics, then measurement precision of security assessment is improved, but device complexity for calculation increases
Solution Approach 1:
The patent divides the fuzzy calculation workload into four separate detection modules, each responsible for one type of fuzzy set calculation (attack detection, error rate, repetition rate, miss rate). This segmentation allows each module to be optimized independently and reduces the complexity burden on any single component, while the overall system achieves comprehensive precision through the combination of all modules.
Solution Approach 2:
The patent employs a universal trapezoidal fuzzy set framework that can handle multiple different security metrics (attack counts, error rates, repetition rates, miss rates) using the same mathematical structure and membership function approach. This universal methodology reduces calculation system complexity by reusing the same fuzzy logic infrastructure across different detection tasks, rather than developing separate complex systems for each metric.
Data Source
AI summary
A method and device for detecting security of Internet of Things data of market transaction, and an electronic apparatus are provided. The method includes: calculating at least one first trapezoidal fuzzy set having a number of attacks of data; calculating at least one second trapezoidal fuzzy set of an error rate of the data; calculating at least one third trapezoidal fuzzy set of a repetition rate of the data; calculating at least one fourth trapezoidal fuzzy set of miss rate of the data; determining a security detection value according to the first trapezoidal fuzzy set; determining a false detection value according to the second trapezoidal fuzzy set, the third trapezoidal fuzzy set and the fourth trapezoidal fuzzy set; and determining security of the Internet of Things data of the market transaction according to the security detection value and the false detection value.