Machine-Readable Code Risk Quantification for Threat Detection

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Solution Overview

Problem

Existing cybersecurity solutions fail to address the security threats and risks associated with scanning machine-readable codes, which can lead to drive-by-download attacks, phishing, smishing, and other malicious activities.

Innovation Solution

A multi-context threat assessment system that analyzes various risk assessment attributes, including code attributes, target network resource attributes, entity attributes, end-user attributes, and enterprise system attributes, to detect, classify, and decode machine-readable codes, thereby quantifying associated security risks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If existing cybersecurity solutions are used, then general security protection is provided, but security threats and risks associated with machine-readable codes cannot be detected or assessed

Engineering Contradiction:

Improvesecurity protection capabilityVSAvoidcode-specific threat detection capability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The security assessment system is divided into specialized modules: a machine-readable code scanning module, a risk assessment module, and a threat detection module. Each module handles specific aspects of code security, enabling comprehensive protection while maintaining focused functionality for code-specific threats.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The security application integrates multiple functions into a single platform: general cybersecurity protection, machine-readable code scanning, risk quantification, and threat assessment. This multi-functional system addresses both general security needs and specific code-related vulnerabilities.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Measurement precision

If comprehensive risk assessment of multiple contextual parameters is performed, then accurate security threat detection is achieved, but system complexity increases

Engineering Contradiction:

Improverisk quantification accuracyVSAvoidassessment system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The risk assessment process is segmented into distinct evaluation categories: code attributes, target network resource attributes, entity attributes, end-user attributes, and enterprise system attributes. This segmentation allows comprehensive assessment while organizing complexity into manageable, structured components.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system evaluates multiple contextual parameters simultaneously, transforming diverse input data into a quantified risk score. By changing parameters from qualitative assessments to quantitative measurements, the system achieves precise risk quantification while maintaining systematic complexity management.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If multiple contextual risk factors are evaluated, then comprehensive security assessment is achieved, but processing time and computational resources increase

Engineering Contradiction:

Improvesecurity assessment completenessVSAvoidcode scanning and assessment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary risk assessments by evaluating code attributes and basic parameters before conducting full multi-context analysis. This staged approach allows quick initial screening followed by detailed assessment only when necessary, reducing overall processing time while maintaining comprehensive evaluation capability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The risk assessment operates continuously, with the system maintaining persistent evaluation of code parameters and updating risk scores in real-time as additional contextual information becomes available. This continuous action eliminates redundant processing steps and optimizes computational resource utilization.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS20250294047A1Cybersecurity architectures for multi-contextual risk quantification

Publication Date: 2025.09.18 QRSHIELD LLC

AI summary

The present disclosure relates to cybersecurity architectures and systems for assessing and quantifying security threats and risks associated with machine-readable codes, such as quick response codes, barcodes, data matrix codes, and other types of codes. A security application comprises a multi-context threat assessment system configured to analyze a broad spectrum of risk assessment attributes across multiple contexts. These contexts relate to the machine-readable code itself, target network resources identified by the code, entities affiliated with the code, end-users interacting with the code, and enterprise systems policies. The system can evaluate various risk assessment attributes for each of these contexts to more accurately quantify potential security risks associated with the machine-readable codes. The security application further includes an API for extending its threat assessment capabilities to various digital ecosystems and an AI-powered learning network comprising language models and computer vision systems to enhance threat detection and risk quantification capabilities.