Automated Blockchain Knowledge Repositories for Malicious Wallet Tracing
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Solution Overview
Problem
Existing blockchain systems face challenges in managing anonymous wallet addresses, which can be exploited by malicious actors, leading to difficulties in tracing and mitigating blockchain-related cyberthreats such as phishing and ransomware attacks.
Innovation Solution
A system that automates the creation of a knowledge repository by linking blockchain wallet addresses to external knowledge sources, using self-learning processes to identify and update the repository with metadata from content items associated with malicious wallet addresses, enabling threat detection and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blockchain wallet addresses are kept anonymous to maintain privacy, then user privacy is improved, but ability to detect and trace malicious activities deteriorates
Solution Approach 1:
The patent introduces an intermediary system that links wallet addresses to external knowledge sources without directly exposing user identities. The system creates associations between blockchain addresses and metadata from content items, enabling threat detection while preserving the anonymity of individual users. This intermediary layer allows tracing of malicious patterns without compromising privacy.
Solution Approach 2:
The system implements feedback mechanisms where detected malicious activities and associated wallet addresses are fed back into the knowledge repository. This enables continuous learning and improvement of detection capabilities, allowing the system to identify and trace malicious patterns more effectively over time while maintaining user privacy through automated updates of the knowledge base.
2Measurement precision
If manual curation of blockchain knowledge repository is used, then quality of threat detection is improved, but system productivity and scalability deteriorate
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically curates and updates its own knowledge repository. The system autonomously extracts metadata from content items, associates them with wallet addresses, and updates threat detection models without requiring manual intervention. This self-service capability maintains high detection quality while enabling rapid scalability.
Solution Approach 2:
The system maintains continuous operation by automatically processing new content items and updating the knowledge repository in real-time. The useful action of threat detection continues without interruption, as the system continuously learns from new data and updates its models, ensuring both high quality and scalability.
3Productivity
If automated extraction of metadata from content items is implemented, then system productivity is improved, but complexity of data processing increases
Solution Approach 1:
The patent segments the complex data processing task into distinct manageable components: content item retrieval, metadata extraction, wallet address association, and knowledge repository updates. Each segment can be processed independently and optimized separately, reducing overall complexity while maintaining high productivity through automation.
Data Source
AI summary
In one example aspect, a first content item is received. First metadata from the first content item; detecting a match between the first metadata and at least one predetermined metadata element in a knowledge repository pertaining to a target blockchain context; determining a first blockchain wallet address associated with the first content item; locating a second content item based on the first blockchain wallet address; extracting second metadata from the second content item; and updating the knowledge repository based on the second metadata extracted from the second content item.