Opcode-Based Machine Learning for Malicious Encryption Detection
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Solution Overview
Problem
Existing malware detection methods struggle to effectively identify malicious encryption, particularly ransomware, which encrypts user files and demands payment for access, often relying on outdated signature-based approaches that are easily evaded by modern malware variants.
Innovation Solution
A computer-implemented method using a machine learning model trained on opcode data to classify processes as malicious or benign, where opcode sequences are analyzed to determine if they indicate malicious encryption, and the model is deployed to restrict or halt such processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based approaches are used for malware detection, then detection simplicity is maintained, but detection effectiveness deteriorates as malware variants evolve and evade signatures
Solution Approach 1:
The patent replaces traditional signature-based detection mechanisms with machine learning models that analyze opcode sequences. Instead of relying on predefined signatures, the system uses trained models to automatically identify malicious patterns, substituting mechanical rule-based approaches with intelligent adaptive systems that improve detection effectiveness against evolving malware variants
Solution Approach 2:
The patent changes the detection parameters from signature matching to opcode sequence analysis. By transforming the input data into opcode sequences and using machine learning models to analyze these sequences, the system detects malicious encryption behaviors based on operational patterns rather than static signatures, enabling detection of previously unknown malware variants
2Speed
If real-time opcode analysis is performed, then detection speed is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on extensive opcode datasets before deployment. The models are trained offline to recognize malicious patterns, so that during real-time detection, the system only needs to execute the pre-trained models on incoming opcode sequences, significantly reducing online computational complexity while maintaining fast detection speed
Solution Approach 2:
The patent segments the detection process into distinct phases: opcode collection, sequence generation, model inference, and classification. This segmentation allows each component to be optimized independently, with machine learning models processing only relevant features from opcode sequences, reducing overall computational complexity while enabling real-time analysis
3Measurement precision
If machine learning models are trained on opcode data, then detection accuracy is improved, but training data requirements increase
Solution Approach 1:
The patent extracts essential features from raw opcode data by generating standardized opcode sequences that capture the critical behavioral patterns of malware. This extraction process transforms voluminous raw opcode data into condensed, informative sequences that retain the essential characteristics needed for accurate classification, reducing the effective training data volume while maintaining or improving detection accuracy
Solution Approach 2:
The patent uses synthetic opcode sequences generated from known malware behaviors as training data. By creating copies and variations of malicious opcode patterns, the system can train machine learning models on diverse examples without requiring proportionally more real-world malware samples, efficiently achieving high accuracy with manageable training data volumes
Data Source
AI summary
A computer-implemented method, computer program product, and computer system to classify executing processes as malicious based on opcode data. A processor to retrieves live opcode data corresponding to one or more processors executing a process. A processor generates a malicious classification of the process based on output from a machine learning model that is provided the live opcode data. A processor, in response to the machine learning model classifying the process as malicious, restricts the execution of the process on the one or more processors.