Malware Data Transformation for Secure Machine Learning
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Solution Overview
Problem
Existing machine learning systems using malware as training data are vulnerable to attacks and compromised by the malware, leading to security risks and interference from antivirus software.
Innovation Solution
Generating post-replacement data by replacing malware values with other values using bijective relationships while maintaining predetermined characteristics, creating learning data that prevents executable malware and avoids signature matching with antivirus software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If malware is used as learning data for machine learning, then the learning model can be trained with real malware characteristics, but the computer performing machine learning is exposed to security risks and attack using the malware
Solution Approach 1:
The patent creates a copy of the malware data that preserves the essential characteristics needed for machine learning while removing the harmful executable content. The copying process transforms the malware into a safe representation that can be used for training without posing security risks to the learning system.
Solution Approach 2:
The patent introduces an intermediary processing step that transforms raw malware data into a safe format for machine learning. This intermediary process acts as a mediator between the harmful malware and the vulnerable learning system, converting the data into a form that preserves learning value while eliminating security threats.
2Measurement precision
If malware is used as learning data, then the model can learn malware patterns, but antivirus software may interfere with the machine learning process by detecting and blocking the malware
Solution Approach 1:
The patent creates a copy of malware data that retains the patterns and characteristics needed for training detection models, while the copied data is in a format that antivirus software does not recognize as threats, thus avoiding interference with the learning process.
Solution Approach 2:
The patent transforms the malware data by changing its parameters or representation format, such that the essential malware characteristics are preserved for learning purposes while the transformed parameters prevent antivirus software from detecting and blocking the data during the machine learning process.
3Quantity of substance
If malware is used for machine learning, then training data is available, but the system is vulnerable to attacks and compromised by the malware
Solution Approach 1:
The patent creates safe copies of malware data that can be used abundantly for training purposes without compromising system security. The copying process ensures that multiple training samples can be generated while maintaining system integrity and protection against malware attacks.
Solution Approach 2:
The patent converts the harmful malware data into a beneficial training resource by transforming it into a safe format. The previously harmful malware is turned into a useful training dataset that improves machine learning models while the transformation process eliminates the security risks, effectively converting a threat into an asset.
Data Source
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AI summary
The present invention relates to an information processing program including instructions which, when the program is executed by a computer, cause the computer to perform processing, the processing including: generating post-replacement data by replacing values, with other values, of individual unit data pieces, which have a predetermined data length, of malware in accordance with a replacement rule by which replacement is performed in bijective relationships on a unit data piece basis while a predetermined characteristic indicated in the malware is maintained; and generating, based on the post-replacement data, machine learning data to be used for machine learning in which the predetermined characteristic is used.