Active Learning Framework for Unknown PE Malware Detection
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Solution Overview
Problem
Current machine learning-based malware detection systems face challenges in efficiently detecting unknown portable executable (PE) malware due to concept drift, requiring frequent updates and manual labeling, which is resource-intensive and inefficient, especially with the rapid creation of new malware.
Innovation Solution
A dynamic analysis and temporal patterns-based active learning framework that utilizes API call multi-variate time series data to extract time-interval temporal patterns, enabling the identification of statistically significant discriminative patterns and reducing the need for manual labeling through automated selection and retraining of detection models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning-based detection models are used to detect unknown malware, then detection accuracy is improved, but the models require frequent updates due to concept drift, reducing their long-term effectiveness
Solution Approach 1:
The patent implements a dynamic update mechanism that continuously adapts the detection model to new malware variants. The system automatically updates the model parameters and feature extraction methods in response to emerging malware patterns, preventing concept drift and maintaining high detection accuracy over time without requiring manual retraining.
Solution Approach 2:
The system incorporates feedback loops that monitor detection performance and automatically adjust the model based on new data. When new malware samples are detected, the system uses this feedback to refine the detection model, ensuring continuous improvement and sustained effectiveness against evolving threats.
2Adaptability or versatility
If detection models are frequently updated with new malware data, then detection capabilities are improved, but manual labeling of new data requires significant human expertise and resources
Solution Approach 1:
The system performs self-labeling by automatically determining the maliciousness of new malware samples using the detection model's predictions. This eliminates the need for manual expert labeling, allowing the system to update itself with new data autonomously while maintaining high detection capabilities.
Solution Approach 2:
The patent replaces the mechanical process of manual expert labeling with an automated computational system. The detection model automatically processes and labels new malware data, substituting human expertise with algorithmic decision-making that scales efficiently without additional resource requirements.
3Measurement precision
If the malware signature repository is updated with new data, then detection accuracy is improved, but the computational resources and time required for retraining increase significantly
Solution Approach 1:
Instead of retraining the entire detection model with all historical data, the system applies partial updates using only the most relevant new malware samples. This selective updating approach maintains detection accuracy while significantly reducing the computational resources and time required compared to full retraining.
Solution Approach 2:
The system updates specific model parameters and weights rather than recomputing the entire model. By changing only the necessary parameters based on new data, the system achieves improved detection accuracy with minimal computational overhead, avoiding the resource-intensive process of complete model retraining.
Data Source
AI summary
Provided herein are systems and methods for detecting unknown portable executable (PE) malware utilizing dynamic analysis and temporal patterns. More specifically, the systems and methods provided herein utilize active learning. for enhanced detection of malware in the short and long term based on dynamic analysis and temporal patterns.