Raw-Byte CNN Malware Classification for Polymorphic Variants
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Solution Overview
Problem
Existing malware detection methods struggle with polymorphic malware, which changes appearance and signature files to evade detection, leading to challenges in accurate classification and categorization, especially when using machine learning models.
Innovation Solution
Utilizing convolutional neural networks (CNNs) for malware classification directly on raw byte sequences, without domain-specific feature extraction, and optimizing the CNN architecture and hyperparameters for improved accuracy in identifying polymorphic malware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for malware classification, then they can process malware samples, but they fail to accurately identify polymorphic malware due to inability to handle cryptographically distinct signatures
Solution Approach 1:
The patent replaces traditional machine learning models with convolutional neural networks (CNNs) that process raw byte data directly. This substitution enables the system to handle polymorphic malware by capturing structural patterns in binary code rather than relying on cryptographic signatures, thereby achieving both high classification accuracy and adaptability to polymorphic variants.
Solution Approach 2:
The patent changes the input parameter representation from cryptographic signatures to raw byte sequences. This parameter transformation allows the CNN to analyze the actual structural patterns of malware code, enabling accurate identification of polymorphic malware while maintaining high classification precision across diverse sample types.
2Ease of manufacture
If handcrafted features are used for malware classification, then feature extraction is straightforward, but the process becomes time-consuming and complex when dealing with polymorphic malware
Solution Approach 1:
The patent extracts discriminative features automatically through the CNN architecture itself, eliminating the need for manual feature engineering. The network learns optimal feature representations directly from raw byte data, reducing both the time and complexity associated with feature extraction while maintaining effectiveness against polymorphic malware.
Solution Approach 2:
The CNN system performs self-feature-extraction by automatically learning relevant patterns from raw byte data during training. This self-service capability eliminates the need for external manual feature engineering, significantly reducing the time and effort required while adapting to polymorphic malware variants.
3Loss of time
If raw byte data is processed directly by CNNs, then feature extraction is automated and time is saved, but the system must handle vast numbers of cryptographically distinct samples
Solution Approach 1:
The CNN architecture provides universal processing capability that handles both known and unknown malware variants through a single unified model. This multi-functionality allows the system to process vast numbers of cryptographically distinct samples without requiring separate handling procedures, automatically learning patterns that generalize across diverse malware families.
Solution Approach 2:
By transforming the data representation from cryptographic signatures to raw byte sequences, the system changes the parameter space to one where structural patterns remain consistent across polymorphic variants. This enables the CNN to efficiently process vast numbers of distinct samples by focusing on invariant structural features rather than variable cryptographic identifiers.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes at least one memory, instructions; and processor circuitry to execute the instructions to train a neural network with a plurality of raw byte data samples, perform feature extraction on ones of the plurality of raw byte data samples, determine whether ones of the plurality of raw byte data samples are clean or malicious using the extracted features, and determine a family of malware to which an identified malicious sample belongs.