Graph Neural Network Malware Detection via Behavioral Graphs
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Solution Overview
Problem
Existing malware detection methods face challenges in identifying malicious behavior in computer code, as malware developers use obfuscation techniques to hide malicious code, making it difficult to differentiate between benign and malicious behavior, which is computationally expensive and requires significant resources.
Innovation Solution
The use of graph neural networks to represent computer instructions as graphs, train on malicious and clean behavior patterns, and determine the relatedness between these graphs to identify potentially malicious activity, employing a triplet loss function to distinguish between malicious and clean behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional code comparison methods are used to detect malware, then known malicious code can be identified, but obfuscated malware can evade detection
Solution Approach 1:
The patent replaces traditional mechanical code comparison methods with a graph neural network-based machine learning system. Instead of directly comparing code strings or byte sequences, the system converts code behavior into graph representations and uses GNNs to learn patterns, thereby overcoming the limitations of rule-based and signature-based detection methods against obfuscated malware.
Solution Approach 2:
The patent changes the detection parameters from code-level features (byte sequences, opcodes) to behavior-level features (system calls, API calls, execution flow). By transforming the input data into graph representations where nodes represent code elements and edges represent control flow or data flow, the system detects malware based on behavioral patterns rather than static code characteristics, making obfuscation ineffective.
2Measurement precision
If behavioral analysis is used to detect malware, then obfuscated malware can be detected, but computational complexity increases significantly
Solution Approach 1:
The patent segments the code behavior analysis into distinct graph construction phases and GNN processing phases. The system divides the behavioral data into graph elements (nodes and edges) representing specific code components and their relationships, then processes these segmented graphs through the neural network. This segmentation makes the complex computational task more manageable and efficient.
Solution Approach 2:
The patent creates graph representations as simplified copies of the actual code execution behavior. Instead of analyzing the full complexity of running code, the system generates graph models that capture essential behavioral patterns (control flow, data flow, system interactions) in a condensed format that is more efficient for computational analysis while retaining detection accuracy.
3Measurement precision
If complete code execution is monitored for malware detection, then accurate detection is achieved, but analysis time increases
Solution Approach 1:
The patent applies partial action by monitoring and analyzing only the most relevant behavioral aspects of code execution rather than every single instruction. The graph neural network focuses on capturing essential behavioral patterns (key system calls, critical API interactions, significant control flow changes) while ignoring redundant or less informative execution details, thereby reducing analysis time while maintaining detection accuracy.
Data Source
AI summary
A method of detecting likely malicious activity in a sequence of computer instructions includes identifying a set of behaviors of the computer instructions and representing the identified behaviors as a graph. The graph is provided to a graph neural network that is trained to generate a geometric representation of the sequence of computer instructions, and a degree of relatedness between the geometric representation of the computer instructions and a set of base graphs including base graphs known to be malicious is determined. The sequence of computer instructions is determined to likely be malicious or clean based on a degree of relatedness between the geometric representation of the computer instructions and one or more base graphs known to be malicious.