Singleton File Classification via Entropy and Path Analysis
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Solution Overview
Problem
Traditional security software struggles to identify potentially malicious singleton files with unique attributes, often failing to detect them due to limited information and incorrectly flagging benign files as malware, leading to inaccurate classifications.
Innovation Solution
The system classifies files by identifying sets of benign and malicious singleton files, extracting features to train a classifier, which analyzes unknown files to determine their maliciousness and triggers security actions, using file identification information such as filenames, paths, and entropy to improve recognition of benign files and prevent harm to computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security software uses known malware databases to detect malicious files, then detection accuracy for known malware is improved, but detection capability for singleton files with unique attributes deteriorates
Solution Approach 1:
The system performs preliminary classification of singleton files by analyzing file attributes (size, extension, entropy, path) before executing them. This preliminary action enables the system to identify potentially malicious singleton files based on their characteristics rather than requiring prior knowledge in malware databases, thus resolving the contradiction between detection accuracy for known malware and detection capability for unique singleton files
Solution Approach 2:
The system changes the detection parameters from relying on known malware signatures to analyzing intrinsic file parameters such as file size, extension, entropy, and path characteristics. This parameter change enables the detection of singleton files with unique attributes that cannot be matched against existing malware databases, while maintaining detection accuracy through multi-parameter analysis
2Measurement precision
If security software collects detailed information on singleton files across multiple computing systems, then detection accuracy is improved, but privacy issues and costs increase
Solution Approach 1:
The system extracts only the essential and minimal file attributes (size, extension, entropy, path) needed for classification purposes, rather than collecting comprehensive detailed information about singleton files. This extraction approach maintains detection accuracy by focusing on the most discriminative features while minimizing privacy intrusion and information loss
Solution Approach 2:
The system uses lightweight, disposable file attribute data that can be quickly analyzed and discarded after classification, rather than maintaining persistent detailed profiles of singleton files. This approach reduces the burden of information storage and privacy management while enabling effective detection through transient attribute analysis
3Quantity of substance
If security software flags all singleton files with unknown attributes as potential malware, then detection coverage is improved, but false positive rate increases
Solution Approach 1:
The system applies different classification rules and quality thresholds to different file attribute combinations rather than treating all singleton files uniformly. By analyzing specific local characteristics (file path patterns, entropy values, size ranges) and applying context-specific evaluation criteria, the system achieves comprehensive detection coverage while maintaining reliability by reducing false positives through nuanced local analysis
4Measurement precision
If traditional security systems require extensive information about each file for accurate classification, then classification accuracy is improved, but processing complexity and resource requirements increase
Solution Approach 1:
The system performs partial classification by analyzing only the most critical file attributes (size, extension, entropy, path) rather than examining all possible file characteristics. This partial action approach maintains sufficient classification accuracy for singleton files by focusing on the most discriminative features, thereby reducing processing complexity and resource requirements while avoiding the need for exhaustive file analysis
Data Source
AI summary
A computer-implemented method for identifying potentially malicious singleton files may include (1) identifying a set of benign singleton files and a set of malicious singleton files, (2) obtaining, for each singleton file in the sets of benign and malicious singleton files, file identification information that identifies the singleton file, (3) using the file identification information of the singleton files from the sets of benign and malicious singleton files to train a classifier to classify unknown singleton files, (4) detecting an unclassified singleton file, (5) analyzing, with the trained classifier, information that identifies the unclassified singleton file, (6) determining, based on the analysis of the information that identifies the unclassified singleton file, that the unclassified singleton file is suspicious, and (7) triggering a security action in response to determining that the unclassified singleton file is suspicious. Various other methods, systems, and computer-readable media are also disclosed.