Malware Detection for On-Chain Cryptographic Tokens
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Solution Overview
Problem
The proliferation of cryptographic tokens created by malicious on-chain programs poses a challenge in determining whether new tokens are associated with malicious intent, as existing systems lack effective mechanisms to differentiate between legitimate and malicious tokens.
Innovation Solution
A malware detection system utilizing a machine learning model to identify malicious on-chain programs by processing attribute sets from cryptographic tokens, which includes attributes like creation block data, number of holders, and transfer duration, and generating warnings or preventing transactions with malicious tokens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a malware detection system uses traditional rule-based methods to identify malicious tokens, then the system structure remains simple, but the detection accuracy and adaptability to new malicious patterns are insufficient
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component between the attribute extraction module and the detection database. This model processes token attributes and generates detection scripts, bridging the gap between raw data and malicious pattern identification. The intermediary handles the complexity of pattern recognition, allowing the overall system to achieve high detection accuracy without requiring complex rule-based logic throughout the entire system.
Solution Approach 2:
The patent replaces traditional mechanical rule-based detection mechanisms with a machine learning-based intelligent system. Instead of manually configuring detection rules, the system uses trained models to automatically analyze token attributes and generate detection scripts. This substitution enables the system to adapt to new malicious patterns dynamically while maintaining a relatively simple overall structure.
2Reliability
If the system manually creates detection scripts for each malicious pattern, then the detection rules are precise, but the time and resources required to update and maintain the system increase significantly
Solution Approach 1:
The system implements self-service through automated script generation. When the machine learning model detects new malicious patterns, it automatically generates corresponding detection scripts and updates the detection database without human intervention. This self-service capability maintains high detection reliability by ensuring scripts are created based on actual detected patterns, while dramatically reducing the time and resources required for system maintenance compared to manual script creation.
Solution Approach 2:
The system incorporates feedback loops where detection results and new malicious patterns are fed back into the machine learning model for continuous learning and improvement. The model uses feedback from detected malicious tokens to refine its understanding and generate more accurate detection scripts. This feedback mechanism ensures detection reliability improves over time while automating the maintenance process, reducing the need for manual updates.
3Measurement precision
If the system analyzes comprehensive attribute sets for each token, then the detection thoroughness is high, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing and organizing token attributes into structured formats before analysis. The machine learning model receives pre-processed attribute sets that are already organized and filtered, reducing the computational burden during actual detection. This preliminary preparation allows the system to maintain high detection thoroughness by analyzing comprehensive attributes while improving processing speed through efficient data preparation.
Solution Approach 2:
The detection process is segmented into multiple stages: attribute extraction, preliminary filtering, machine learning analysis, and script generation. Each stage processes only the necessary attributes for its specific function, avoiding redundant analysis of all attributes throughout the entire pipeline. This segmentation allows the system to maintain thorough detection by analyzing relevant attributes at each stage while improving overall processing efficiency by avoiding unnecessary computations.
Data Source
AI summary
Methods and systems are described herein for a malware detection system that detects whether an on-chain program associated with a cryptographic token is malicious based on output of a machine learning model. The malware detection system may retrieve or generate attributes associated with an on-chain program and input those into a plurality of scripts within a malware detection script database to determine whether the on-chain program is malicious. The scripts may be generated based on an output of a machine learning model indicating whether the on-chain program is malicious.