ML Model Malware Detection Through Container Behavior Analysis
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Solution Overview
Problem
Pre-trained machine learning models are susceptible to malicious code, posing a threat due to their access to sensitive data, and existing static scanning tools struggle to detect emerging threats effectively.
Innovation Solution
A dynamic scanning approach within a software container environment is employed to monitor system calls of the ML model, comparing them to safe and suspicious lists to identify potentially malicious behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static scanning tools are used to detect malware in ML models, then the scanning process is simple and fast, but the detection precision is insufficient for emerging threats
Solution Approach 1:
The patent introduces a software container as an intermediary environment between the ML model and the host system. The container isolates the ML model's execution, allowing safe monitoring of system calls without direct access to sensitive host resources. This intermediary layer enables dynamic behavior analysis while maintaining system security and stability.
Solution Approach 2:
The patent segments the scanning process into multiple components: (1) containerization of the ML model execution environment, (2) system call interception and logging, (3) suspicious call identification through comparison with safe lists, and (4) result aggregation. This segmentation allows each component to be optimized independently and improves overall detection precision.
2Measurement precision
If dynamic scanning within container environment is employed to monitor system calls, then detection precision improves, but the scanning time and computational resources increase
Solution Approach 1:
The patent monitors only the most critical system calls that are indicative of malicious behavior, rather than logging all system calls. By focusing on a subset of high-value calls (e.g., file access, network connections, process creation), the system achieves high detection precision while minimizing the time and computational overhead associated with full system call monitoring.
Solution Approach 2:
The patent pre-compiles lists of safe system calls and suspicious patterns before execution. During the scanning process, detected system calls are quickly matched against these pre-prepared lists, significantly reducing the time required for analysis compared to performing complex evaluations in real-time.
3Measurement precision
If comprehensive system call monitoring is performed, then detection precision improves, but the complexity of analyzing and categorizing calls increases
Solution Approach 1:
The patent implements a feedback mechanism where detected system calls are compared against known safe lists and suspicious patterns, with results fed back to determine whether the ML model is malicious. This structured feedback loop simplifies the analysis process by providing clear decision criteria rather than requiring complex subjective evaluation of each system call.
Solution Approach 2:
The patent transforms the complex problem of system call analysis by changing the parameter of evaluation from understanding the semantic meaning of each call to matching calls against predefined categories (safe vs. suspicious). This parameter transformation converts a complex analytical task into a simpler pattern-matching operation.
Data Source
AI summary
Techniques for scanning a machine learning (ML) model, which is received over a network, are disclosed. Information associated with the ML model is received. A container image is accessed, based on the information associated with the ML model. A software container is loaded using the container image. The ML model is loaded within the container. A plurality of system calls from the ML model within the container is detected. One or more system calls of the plurality of system calls are categorized as suspicious, e.g., based on the one or more system calls being outside a normal or expected behavior of the ML model. Responsive at least in part on categorizing the one or more system calls of the plurality of system calls to be suspicious, the ML model is tagged as possibly being malicious. A result indicative of the ML model being tagged as possibly being malicious is output.