Recurrent Neural Networks for Malware Feature Extraction
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Solution Overview
Problem
Manual feature engineering for classifiers in malware analysis is burdensome and often misses non-obvious patterns, especially when dealing with variable-length inputs.
Innovation Solution
Using a trained recurrent neural network (RNN) to automatically extract features by computing a hidden state from input sequences, which can then be concatenated with hand-engineered features for a larger classifier, allowing for the detection of malware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual feature engineering is used to simplify learning problems for classifiers, then the classifier can make decisions on fixed-length representations, but the process becomes burdensome and may miss non-obvious patterns
Solution Approach 1:
The RNN automatically extracts features from variable-length malware sequences without requiring manual feature engineering. The model self-learns discriminative features through training, eliminating the burden of manual feature design while maintaining high detection accuracy. The final hidden state h_i serves as automatically discovered features that capture complex patterns in the data.
2Reliability
If manual feature engineering is performed by domain experts to maintain predictive power, then the classifier can achieve satisfactory performance, but the process is time-consuming and limits discovery of non-intuitive patterns
Solution Approach 1:
The patent replaces the mechanical process of manual feature engineering with an automated neural network-based feature extraction system. The RNN automatically transforms variable-length sequences into fixed-length hidden state representations, eliminating the time-consuming manual process while preserving or enhancing predictive power through data-driven feature discovery.
3Ease of operation
If variable-length inputs are transformed into fixed-length representations through manual feature engineering, then the classifier can process the data, but the transformation process is complex and error-prone
Solution Approach 1:
The RNN automatically handles the transformation of variable-length inputs to fixed-length representations through its inherent sequential processing capability. The final hidden state h_i naturally provides a fixed-length representation regardless of input length, eliminating the need for complex manual transformation processes and reducing errors.
Data Source
Figure 1
AI summary
Using a recurrent neural network (RNN) that has been trained to a satisfactory level of performance, highly discriminative features can be extracted by running a sample through the RNN, and then extracting a final hidden state hh where i is the number of instructions of the sample. This resulting feature vector may then be concatenated with the other hand-engineered features, and a larger classifier may then be trained on hand- engineered as well as automatically determined features. Related apparatus, systems, techniques and articles are also described.