Synthesized Malware Training for ML Threat Detection
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Solution Overview
Problem
Existing machine learning models for malware detection struggle with accurately classifying malicious files and programs, especially new, previously unknown threats, due to limited data characterization and the increasing sophistication of malware attacks, which often result in false positives.
Innovation Solution
The method involves synthesizing new records in the machine learning dataset by combining the attributes of known threats with logic that creates new feature vectors, improving the model's ability to detect new threats while reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained using known malware samples, then the model can learn from labeled data, but the model cannot correctly classify new or unknown threats due to insufficient data characterization
Solution Approach 1:
The system performs preliminary actions by synthesizing potential malware samples before actual threats emerge. The synthesis module generates artificial malware instances with known malicious characteristics, creating a preparatory dataset that enables the machine learning model to recognize attack patterns before encountering real unknown threats in production
Solution Approach 2:
The system creates copies of known malware characteristics by synthesizing new malware samples that replicate the essential features and behaviors of known threats. These synthetic copies serve as training data that preserves the malicious patterns without requiring actual malware samples, enabling the model to generalize to unknown variants
2Adaptability or versatility
If attackers automate malware creation with various encryption and obfuscation modules, then malware sophistication increases, but the protection system misses these objects because they have different attributes than predicted
Solution Approach 1:
The synthesis module systematically varies parameters such as encryption algorithms, obfuscation techniques, and code structures to generate diverse malware samples. By changing these parameters across synthetic samples, the system trains the model to recognize malicious behavior across a wide range of attribute variations, improving detection of sophisticated automated malware
Solution Approach 2:
The system applies different local qualities to various parts of the synthesized malware by incorporating diverse encryption modules, obfuscation techniques, and exploitation methods at specific locations within the malware structure. This creates training data that reflects the heterogeneous nature of modern automated malware while maintaining detectable malicious patterns
3Quantity of substance
If the dataset includes only known malware samples, then the model can be trained on labeled data, but metadata that could reveal attack context and relationships is missing
Solution Approach 1:
The synthesis module acts as an intermediary that generates synthetic malware samples with embedded metadata about attack context, relationships, and characteristics. This intermediary process creates training data that bridges the gap between limited known samples and the need for comprehensive attack context, providing the model with rich metadata without requiring exhaustive real-world data collection
Data Source
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AI summary
A system and method for malware classification using machine learning models trained using synthesized feature sets based on features extracted from samples of known malicious objects and known safe objects. The synthesized feature sets act as virtual samples for training a machine learning classifier to recognize new objects in the wild that are likely to be malicious.