Malware Signature Generation via Data Feature Frequency Analysis
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Solution Overview
Problem
Current malware detection techniques face challenges in identifying similar malware samples with low false positive rates, often generating large volumes of false positives when analyzing millions of files, due to the difficulty in distinguishing between various forms of malware.
Innovation Solution
An apparatus and method that utilize a database to store files, a counter module to calculate data feature frequencies, and a signature generation module to create malware signatures based on these frequencies, allowing for the classification of target files as malware when they meet specific criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional malware detection techniques are used to analyze millions of files, then malware identification is attempted, but large volumes of false positives are generated
Solution Approach 1:
The malware detection process is segmented into multiple stages: initial filtering using lightweight features (file headers, metadata), intermediate analysis using statistical features from file contents, and final deep inspection only for suspicious files. This segmentation allows rapid processing of millions of files while maintaining high detection accuracy by applying comprehensive analysis only where needed.
Solution Approach 2:
The system dynamically adjusts detection parameters and thresholds based on the analysis stage and file characteristics. For example, different threshold values are applied at different filtering stages, and detection sensitivity is adjusted based on the confidence level from previous analysis stages, thereby reducing false positives while maintaining detection effectiveness.
2Measurement precision
If comprehensive malware analysis is performed on all files, then detection accuracy improves, but processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary analysis using fast, lightweight features such as file headers, metadata, and basic statistical properties before conducting more computationally intensive analysis. Files that pass the preliminary screening are quickly classified as benign, while only suspicious files proceed to deeper analysis stages, significantly reducing overall processing time.
Solution Approach 2:
The system applies partial analysis to all files (lightweight feature extraction) and excessive/detailed analysis only to a small subset of suspicious files. This selective approach ensures that the majority of files are processed rapidly with minimal computational overhead, while maintaining high detection accuracy for malicious files through targeted deep inspection.
3Productivity
If simple detection criteria are used, then processing speed increases, but the ability to distinguish malware from benign files decreases
Solution Approach 1:
The system transitions from single-dimensional detection (simple signature matching) to multi-dimensional analysis by incorporating diverse feature types across different dimensions: structural features (file format, headers), statistical features (entropy, character distribution), behavioral features (API calls, execution patterns), and contextual features (metadata, file relationships). This dimensional expansion enables accurate malware distinction while maintaining processing efficiency through hierarchical feature evaluation.
Data Source
AI summary
An apparatus includes a database configured to store a collection of files. The apparatus also includes a counter module configured to calculate a frequency of a data feature in the collection of files. The apparatus also includes a signature generation module operatively coupled to the counter module. The signature generation module is configured to generate a malware signature based on the frequency of the data feature in the collection of files. The malware signature includes an indication of one or more criterion for the data feature, and the malware signature is associated with a malware. The apparatus also includes a communication module configured to receive a target file, and a detection module operatively coupled to the communication module. The detection module is configured to classify the target file as the malware when the target file meets the one or more criterion of the malware signature.