Ransomware Detection Using CNN File Typing and Byte Analysis
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Solution Overview
Problem
Existing ransomware detection methods are ineffective against sophisticated attacks that evade traditional heuristic and static analysis-based solutions, as they can bypass access control policies and maintain file integrity by incremental encryption without changing file extensions, making it difficult to detect and mitigate ransomware attacks.
Innovation Solution
A ransomware mitigation engine utilizing a convolutional neural network (CNN) for file type identification (FTI) and a statistical analysis heuristic layer to analyze file content, determining file integrity and identifying potential ransomware attacks by examining byte distributions, with a file system agnostic approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional heuristic and static analysis-based solutions are used for ransomware detection, then device complexity is reduced and ease of operation is improved, but detection reliability deteriorates as sophisticated ransomware attacks can evade these methods
Solution Approach 1:
The patent replaces traditional heuristic and static analysis-based detection mechanisms with a convolutional neural network (CNN) model. This substitution transitions from rule-based mechanical detection to a learned statistical model that analyzes byte-level patterns in file contents, enabling detection of sophisticated ransomware attacks that evade traditional methods while maintaining system operability.
Solution Approach 2:
The patent changes the detection parameters from high-level file attributes and behavioral heuristics to low-level byte distribution statistics. By analyzing the statistical properties of byte sequences in file contents rather than relying on file metadata or access patterns, the system achieves higher detection reliability for encrypted files while managing complexity through efficient statistical computations.
2Measurement precision
If byte-level statistical analysis is performed on all files to detect ransomware, then detection precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by performing byte-level statistical analysis selectively rather than on all files. The CNN model processes only files that require detailed inspection based on initial screening, analyzing byte distribution patterns in targeted files to detect ransomware while avoiding unnecessary processing of benign files, thus balancing detection precision with processing efficiency.
Solution Approach 2:
The patent extracts only the essential byte-level statistical features needed for ransomware detection from file contents, rather than performing complete file analysis. By computing key byte distribution metrics and feeding them to the CNN model, the system achieves high detection precision while minimizing processing time and computational resource consumption.
Data Source
AI summary
There is disclosed in one example a ransomware mitigation engine, including: a processor; a convolutional neural network configured to provide file type identification (FTI) services including: identifying an access operation of a file as a write to the file or newly creating the file; computing a byte correlation factor for the file; classifying the file as belonging to a file type; determining with a screening confidence that the file type is correct for the file; determining that the screening confidence is below a screening confidence threshold; and circuitry and logic to provide heuristic analysis including: receiving notification that the confidence is below the confidence threshold; performing a statistical analysis of the file to determine a difference between an expected value and a computed value; determining from the difference, with a detection confidence, that the file has been compromised; and identifying the file as having been compromised by a ransomware attack.