Incremental Malware Detection Model Fine-Tuning
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Solution Overview
Problem
Machine learning models used for malware detection become less accurate over time due to the rapid evolution of malware, leading to inefficiencies in retraining and potential catastrophic forgetting of original classification abilities.
Innovation Solution
A method for sequentially fine-tuning machine learning models using a mix of old and new data, with weighting samples by age, to maintain accuracy against a changing malware landscape without full retraining, allowing for efficient updates that preserve classification capabilities for both new and known threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full retraining of the machine learning model is performed to maintain accuracy against new malware, then detection accuracy for emerging threats is improved, but computational expense and time consumption increase significantly
Solution Approach 1:
The patent segments the retraining process into incremental updates using only new malware samples rather than retraining the entire model from scratch with all historical data. This segmentation allows the model to be updated efficiently with partial data (new samples) while maintaining overall detection accuracy, resolving the contradiction between maintaining reliability and reducing time loss.
Solution Approach 2:
The patent applies partial action by using only a subset of training data (new malware samples) rather than the complete dataset for retraining. This partial retraining approach is sufficient to maintain detection accuracy against emerging threats without requiring the excessive computational effort of full retraining, thus reducing time consumption while preserving reliability.
2Productivity
If incremental retraining with only new data is performed to reduce computational expense, then efficiency is improved, but catastrophic forgetting occurs where the model loses ability to classify previously known threats
Solution Approach 1:
The patent applies preliminary action by periodically retraining the model with complete historical data at predetermined intervals or when performance degradation is detected. This preliminary full retraining prevents catastrophic forgetting by periodically refreshing the model's knowledge of all malware types, while between these periods the model benefits from efficient incremental updates with new data, thus maintaining both productivity and reliability.
3Reliability
If frequent full retraining is performed to maintain high detection accuracy, then reliability is improved, but computational resources and costs increase
Solution Approach 1:
The patent implements periodic action by scheduling full retraining operations at predetermined time intervals or trigger events (such as detected performance degradation) rather than performing frequent continuous full retraining. Between these periodic full retraining events, the model uses efficient incremental updates with new samples. This periodic approach maintains detection accuracy while significantly reducing overall computational energy consumption compared to frequent full retraining.
Data Source
AI summary
A machine learning model is sequentially fine-tuned with new training data as the training data becomes available. By using a suitable mix of old and new data, and weighting samples in the training data by age, the model can be efficiently updated to maintain accuracy against a changing malware landscape without manual modifications of the network layers or the computational expense of full retraining.