Machine Learning Malware Detection with Order-Independent File Features
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Solution Overview
Problem
Existing malware detection systems are time-consuming, resource-intensive, and prone to false positives due to reliance on file type knowledge and byte order changes, requiring frequent updates and inefficient resource usage.
Innovation Solution
A malware detection device uses machine learning techniques to analyze file attributes like informational entropy and byte value variance, independent of file type, through modules such as file compressors, entropy calculators, and threat analyzers to determine malware probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional malware detection systems compare each malware sample to a potential malware file sample using known malware databases, then detection accuracy is improved, but analysis time and resource consumption increase significantly
Solution Approach 1:
The system pre-calculates and stores entropy values and other statistical features for known malware samples during database construction. When a new file needs detection, the system retrieves these pre-computed features and compares them directly, avoiding time-consuming real-time analysis of the entire file content. This preliminary preparation significantly reduces detection time while maintaining accuracy.
Solution Approach 2:
The invention extracts key statistical features (entropy, byte distribution, n-gram frequencies) from malware files and uses only these extracted features for detection comparisons, rather than comparing entire files or performing exhaustive analysis. This extraction approach maintains detection accuracy while dramatically reducing the computational resources and time required.
2Reliability
If rule-based systems use knowledge of expected byte arrangement in files to detect malware, then detection capability is improved, but false positives increase due to sensitivity to natural file modifications
Solution Approach 1:
The system transforms the detection approach by changing from rigid byte-position-based rules to statistical parameter-based detection. Instead of checking if bytes are at specific positions (which fails when files are naturally modified), the system analyzes statistical parameters like entropy, byte distribution patterns, and n-gram frequencies that remain characteristic of malware even when file structure changes. This parameter transformation maintains detection capability while reducing false positives.
Solution Approach 2:
The invention applies different analysis methods to different parts of the file based on their characteristics. Rather than applying a uniform rule-based approach to the entire file, the system analyzes specific local patterns (n-grams, entropy regions, byte distribution zones) that are characteristic of malware. This localized statistical analysis is more robust to natural file modifications while maintaining detection accuracy.
3Adaptability or versatility
If systems maintain knowledge of expected byte arrangement for a large number of file types, then detection coverage is improved, but resource requirements increase significantly
Solution Approach 1:
The system employs a universal statistical analysis framework that works across all file types without requiring type-specific knowledge. The same entropy calculation, byte distribution analysis, and n-gram frequency methods are applied universally to detect malware regardless of file format. This universal approach eliminates the need to maintain separate byte arrangement knowledge for numerous file types, significantly reducing resource requirements while maintaining broad detection coverage.
Solution Approach 2:
The invention changes from maintaining extensive type-specific byte arrangement knowledge to using universal statistical parameters that adapt to any file type. By analyzing entropy, byte distribution, and frequency patterns rather than position-specific byte values, the system achieves broad detection coverage across all file types without requiring large amounts of type-specific reference data, thus reducing resource requirements.
Data Source
AI summary
Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.