Malware Detection via Response Matrix and Filter Segmentation
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Solution Overview
Problem
Conventional anti-malware software struggles to detect polymorphic malware, which changes its signature without altering functionality, making it difficult to identify and remove.
Innovation Solution
The system employs a file analyzer that iteratively applies filters to a file, generating a response matrix, and uses machine learning to create a signature for malware by adding numeric filter results, enabling the classification of malware even with polymorphic variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional signature-based detection is used, then detection simplicity is maintained, but detection accuracy deteriorates due to polymorphic malware variations
Solution Approach 1:
The malware detection system segments the file into multiple windows and applies filters to each window independently, generating a response matrix that captures local patterns. This segmentation allows the system to detect polymorphic malware by identifying consistent filter response patterns across different file segments, even when the malware changes its signature.
Solution Approach 2:
The system transforms the one-dimensional byte sequence of the file into a multi-dimensional response matrix through filter applications. This dimensional transformation creates a new feature space where malware patterns become distinguishable from benign patterns, improving detection accuracy while managing complexity through structured data representation.
2Adaptability or versatility
If polymorphic malware is used, then malware adaptability improves, but detectability deteriorates due to signature changes
Solution Approach 1:
The system applies multiple filters that transform file content into numeric response values, changing the parameter representation from raw bytes to filter-based features. This parameter transformation allows the detection system to identify malware based on behavioral patterns in the response matrix rather than fixed signatures, effectively countering polymorphic variations.
Solution Approach 2:
The patent replaces the mechanical signature-matching approach with a machine learning-based classification system. The classifier learns to identify malware patterns in the response matrix without relying on predefined signatures, substituting rigid mechanical detection with adaptive intelligent detection that can handle polymorphic variations.
3Measurement precision
If iterative filter analysis is applied, then detection precision improves, but processing time increases
Solution Approach 1:
The system performs preliminary filtering and response matrix generation before classification, preparing the data in advance. By pre-processing the file through multiple filters and organizing results into a structured response matrix, the system reduces the complexity of the subsequent classification step, balancing precision with processing efficiency.
Data Source
AI summary
Systems and methods analyze input files to automatically determine malware signatures. A set of input files known to contain a particular type of malware can be provided to a file analyzer. The file analyzer can analyze the file using a sliding window to create vectors from values that are provided by multiple filters that process each window. The vectors created for a file define a response matrix. The response matrices for a set of input files can be analyzed by a classifier to determine useful vector components that can define a signature for the malware.