Secure Messaging Authentication for Non-Credentialed Blockchain Entities
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Solution Overview
Problem
Existing technologies fail to address the complexities of secure interoperability and authentication in distributed collaborative systems, particularly in scenarios involving non-credentialed entities, leading to vulnerabilities such as phishing attacks and human error in data access and compliance.
Innovation Solution
Implementing a blockchain-based system with two-way authentication and machine learning techniques to establish trusted relationships between blockchain applications and non-credentialed entities, enabling secure data transactions and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional authentication systems are used in distributed collaborative systems, then ease of operation is maintained, but security and reliability deteriorate due to vulnerabilities like phishing attacks and human error
Solution Approach 1:
The system segments authentication into two distinct paths: one for credentialed entities using traditional blockchain authentication, and another for non-credentialed entities using machine learning-based anomaly detection. This segmentation allows each path to be optimized independently, maintaining simplicity for standard users while providing enhanced security for untrusted sources.
Solution Approach 2:
The patent introduces an intermediary authentication service that mediates between non-credentialed entities and the blockchain system. This intermediary uses machine learning models to verify the authenticity of requests from untrusted sources without requiring them to have blockchain credentials, thus bridging the trust gap without exposing the core blockchain system to direct security risks.
2Measurement precision
If centralized access to all parties' data is implemented for diagnosis and compliance, then measurement precision is improved, but reliability deteriorates due to huge threat surface and human error
Solution Approach 1:
The system implements local quality by allowing different levels of data access and authentication for different entities. Credentialed entities receive full access with traditional authentication, while non-credentialed entities receive limited access governed by machine learning-based anomaly detection. This localized approach to security enables precise diagnosis capabilities while minimizing the overall threat surface.
Solution Approach 2:
The patent implements feedback mechanisms where machine learning models continuously analyze authentication patterns and system behavior. The models receive feedback from authentication outcomes and system anomalies, adjusting their detection thresholds and strategies to maintain high diagnosis accuracy while adapting to new threat patterns without requiring centralized human intervention.
3Adaptability or versatility
If interoperability between blockchain applications and non-credentialed entities is enabled, then adaptability is improved, but security deteriorates due to lack of authentication for untrusted sources
Solution Approach 1:
The authentication service is designed with universality to handle multiple types of entities through a single unified interface. It can authenticate both credentialed blockchain entities using traditional methods and non-credentialed external entities using machine learning-based anomaly detection, eliminating the need for separate systems and enabling broad interoperability without compromising security.
Solution Approach 2:
The system dynamically changes authentication parameters based on the entity type and request characteristics. For credentialed entities, it uses fixed cryptographic verification parameters. For non-credentialed entities, it adjusts parameters such as anomaly thresholds, confidence levels, and detection sensitivity based on the specific context and historical data, enabling flexible interoperability while maintaining adaptive security.
Data Source
AI summary
Multi-layer ensembles of neural subnetworks are disclosed. Implementations can classify inputs indicating various anomalous sensed conditions into probabilistic anomalies using an anomaly subnetwork. Determined probabilistic anomalies are classified into remedial application triggers invoked to recommend or take actions to remediate, and/or report the anomaly. Implementations can select a report type to submit, or a report recipient, based upon the situation state, e.g., FDA: Field Alert Report (FAR), Biological Product Deviation Report (BPDR), Medwatch, voluntary reporting by healthcare professionals, consumers, and patients (Forms 3500, 3500A, 3500B, Reportable Food Registry, Vaccine Adverse Event Reporting System (VAERS), Investigative Drug/Gene Research Study Adverse Event Reports, Potential Tobacco Product Violations Reporting (Form 3779), USDA: APHIS Center for Veterinary Biologics Reports, Animal and Plant Health Inspection Service: Adverse Event Reporting, FSIS Electronic Consumer Complaints, DEA Tips, Animal Drug Safety Reporting, Consumer Product Safety Commission Reports, State/local reports: Health Department, Board of Pharmacy.