AI-Generated Malware Test 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 and prevent the distribution of infected software.
Innovation Solution
A system using artificial intelligence to mutate malicious code into mutated malware, mimicking obfuscation techniques used by attackers, and iteratively modifying malware scanners to improve detection capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If malware scanners use traditional signature-based detection methods, then detection speed is maintained, but detection accuracy decreases due to polymorphic and metamorphic malware variations
Solution Approach 1:
The system creates synthetic malware samples by copying and mutating existing malware characteristics. The AI model generates mutated malware variants that replicate the behavior patterns of real malware while using transformed code structures, allowing the scanner to be trained on realistic obfuscated samples without accessing actual malicious software.
Solution Approach 2:
The patent replaces traditional mechanical signature-matching algorithms with an AI-based system that uses neural networks to analyze malware behavior patterns. This substitution enables the scanner to understand semantic meaning and detect polymorphic variations that traditional pattern-matching cannot identify.
2Measurement precision
If malware scanners analyze more code variants to improve detection, then detection accuracy increases, but processing time increases
Solution Approach 1:
The system pre-generates and pre-processes a comprehensive dataset of mutated malware variants using AI models before actual scanning occurs. By preparing training data in advance through automated mutation and transformation processes, the scanner can efficiently analyze new samples without performing time-consuming real-time analysis of all possible variants.
3Adaptability or versatility
If the system generates more mutated malware samples to improve detection coverage, then detection capability improves, but computational resources required increase
Solution Approach 1:
The AI system automatically generates, mutates, and processes malware samples without requiring manual intervention or extensive human computational resources. The system self-manages the generation of training data by using AI models to create mutated variants from existing samples, reducing the need for human experts to manually create diverse test cases.
Data Source
AI summary
In some examples, a server injects malicious code into a legitimate software package to create an injected package. The server uses an artificial intelligence to extract a plurality of parts from the injected package and to mutate individual parts of the plurality of parts to create mutated parts. The server assembles the mutated parts to create a mutated malware. A malware scanner determines a risk score associated with the mutated malware. Based at least in part on determining that the score satisfies a predetermined threshold, the server stores the mutated malware in a set of mutated malware and creates at least one additional mutation based on the mutated malware. After determining that a size of the set of mutated malware satisfies a requested size, the malware scanner is modified to increase detection of the malicious code in individual mutated malware in the set of mutated malware.