AI Malware Training Data for Obfuscated Code Detection
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Solution Overview
Problem
Existing malware scanners struggle to detect malicious code in software packages due to polymorphic and metamorphic variations, making it difficult to identify signatures or patterns, especially when malicious code is obfuscated within legitimate software.
Innovation Solution
An AI system generates mutated malware by injecting malicious code into legitimate software packages, mutating parts to obscure the malware's presence, and uses malware scanners to determine a risk score. The mutated malware is used to train the scanner, improving its detection capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If malware scanners use traditional signature-based detection methods, then they can identify known malware patterns, but they fail to detect polymorphic and metamorphic malware that continuously changes their structure
Solution Approach 1:
The patent changes the detection parameters from static signatures to dynamic behavior patterns. The system analyzes malware at multiple levels (syntax, semantics, behavior) and tracks changes across variants, allowing detection of polymorphic and metamorphic malware that continuously modifies its structure while maintaining core malicious functionality.
Solution Approach 2:
The system transitions from static signature matching to dynamic behavior analysis. By monitoring malware execution and observing behavioral patterns across multiple variants, the system can adapt to continuously changing malware structures while maintaining detection accuracy through learned behavioral characteristics.
2Productivity
If malware scanners analyze only surface-level code patterns, then analysis is fast and simple, but they miss obfuscated malware hidden within legitimate software packages
Solution Approach 1:
The patent segments malware analysis into multiple independent levels: surface-level pattern recognition for speed, intermediate semantic analysis for meaning, and deep behavioral analysis for obfuscated code. Each level processes different aspects of the code, allowing fast scanning at surface level while maintaining precision through multi-level analysis that uncovers hidden malicious patterns.
Solution Approach 2:
The system adds multiple analysis dimensions beyond surface code patterns. By analyzing semantic meaning, execution behavior, and cross-referencing with legitimate software patterns, the system creates additional detection dimensions that enable identification of obfuscated malware without sacrificing scan speed through optimized multi-level processing.
3Quantity of substance
If the training data includes only traditional malware samples, then the scanner learns basic malware patterns, but it cannot detect newly generated AI malware with sophisticated obfuscation
Solution Approach 1:
The patent uses AI models to generate synthetic malware samples that replicate advanced obfuscation techniques. These copied malware variants train the scanner on sophisticated patterns without requiring actual attack samples, enabling the system to learn and detect advanced malware behaviors through synthesized training data that mirrors real-world threats.
Solution Approach 2:
The system converts the harmful capability of AI-generated malware into beneficial training data. By capturing and analyzing AI-generated malicious code patterns, the system transforms what would be pure threats into educational samples that teach the scanner to recognize and detect similar advanced malware techniques in the future.
Data Source
AI summary
In some cases, malicious code is injected into a legitimate software package to create an injected package. An artificial intelligence (AI) extracts a plurality of parts of the injected package, mutates individual parts to create multiple mutated parts, and assembles at least a portion of them to create a mutated malware. A malware scanner determines a risk score associated with the mutated malware. If the risk score satisfies a predetermined threshold, the mutated malware is stored in a set of mutated malware. When a number of mutated malware in the set of mutated malware satisfies a requested number, the set of mutated malware are added to a set of training data that is used to train a particular malware scanner that includes a particular artificial intelligence component. After being trained, the particular malware scanner is deployed to detect malware.