CDR-Based Machine Learning for Unknown Malware Detection
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Solution Overview
Problem
Existing malware detection methods struggle to identify unknown or zero-day malware effectively, as they rely on known patterns and behaviors, failing to detect new and evolving threats.
Innovation Solution
Utilizing content disarm and reconstruction (CDR) analysis in conjunction with machine learning algorithms to analyze file content and structure, training models to identify correlations between CDR output and malware, and employing feature importance and SHAP values to determine file risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional malware detection methods (sandboxing, behavioral analysis, static analysis) are used, then detection of known malware is effective, but detection of unknown or zero-day malware fails
Solution Approach 1:
Instead of looking for known malware patterns (traditional approach), the patent inverts the approach by using CDR to reconstruct files and identify deviations from expected structures. The machine learning model is trained on CDR output to detect anomalies that indicate malware, even unknown variants, by comparing against reconstructed benign file patterns.
Solution Approach 2:
The patent introduces CDR analysis as an intermediary between file input and malware detection. The CDR process disassembles and reconstructs files, creating an intermediate representation that reveals structural characteristics. This intermediary step enables the machine learning model to detect malware based on structural patterns rather than relying on pre-existing malware databases.
2Adaptability or versatility
If content disarm and reconstruction (CDR) analysis with machine learning is implemented, then detection of unknown malware improves, but system complexity increases
Solution Approach 1:
The patent implements a universal CDR-based analysis system that handles multiple file types and malware variants through a single machine learning model. The CDR process creates a standardized intermediate representation that can be analyzed uniformly across different file formats, reducing the need for separate detection mechanisms for each file type or malware family.
Solution Approach 2:
The machine learning model is trained on CDR output data to automatically learn malware detection patterns without requiring manual rule creation or updates. The system self-adapts to new threats by learning from training data, reducing the complexity of manual system maintenance and rule updates while maintaining high detection capability.
Data Source
AI summary
File risk and malware detection and classification can be enhanced using machine learning analysis of content disarm and reconstruction (CDR) output. Correlations can be discovered or analyzed between individual elements of such outputs, which can include an XML report. Such correlations can provide useful information on threat intelligence and help validate content disarm and reconstruction. A method can include training machine learning algorithms with a dataset derived from CDR results from test files labelled as malicious or not malicious; instructing algorithms to predict probabilities; and determining correlation between the report items and malware (for example, using the function feature importances and the SHAP value method).