Application Container System Call Reduction via State Profiling
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Solution Overview
Problem
Existing approaches to preventing malicious operations by malware, such as system call reduction and machine learning models, are either broad and leave unnecessary attack vectors open or rely on computationally intensive and frequently updating models.
Innovation Solution
An application container determines its operation state to allow only necessary system calls, profiling and constraining system calls based on booting and running phases, using seccomp filters to restrict system calls dynamically, thereby reducing the attack surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a superset of system calls is provided for all applications, then compatibility across all applications is improved, but the number of attack vectors available to malware increases
Solution Approach 1:
The patent applies local quality by transitioning from a uniform superset of system calls to application-specific subsets. Each application receives a customized system call set tailored to its actual requirements, determined through static analysis of the application's code and runtime behavior monitoring. This localized approach maintains compatibility for each specific application while eliminating unnecessary system calls that would create attack vectors.
Solution Approach 2:
The patent implements partial action by providing only the necessary system calls required by each application rather than the complete superset. Through analysis of application behavior during different operation states (boot-up, execution, shut down), the system identifies and provides only the minimal set of system calls needed, removing excessive or unnecessary calls that could be exploited by malware.
2Object-affected harmful factors
If system call reduction is applied to minimize attack vectors, then security is improved, but application functionality may be compromised
Solution Approach 1:
The patent applies preliminary action by performing static analysis of application code and monitoring application behavior during boot-up and execution phases before fully restricting system calls. This advance analysis identifies the exact system calls an application needs, allowing the container to pre-configure an optimized system call set that ensures functionality while minimizing attack vectors from the outset.
Solution Approach 2:
The patent implements feedback mechanisms by monitoring application execution and observing which system calls are actually invoked during operation. This runtime feedback is used to refine and adjust the system call restrictions dynamically, ensuring that necessary calls remain available while blocking unnecessary ones, thus maintaining reliability while improving security.
3Measurement precision
If machine learning models are used to detect malware, then detection accuracy is improved, but computational resources and update frequency requirements increase
Solution Approach 1:
The patent replaces complex, resource-intensive machine learning models with simpler, more efficient static analysis techniques and runtime monitoring. Instead of using computationally heavy ML algorithms that require frequent updates and significant processing power, the system employs lightweight analysis methods that examine application code structure and execution patterns, achieving effective malware detection with minimal computational resources.
Data Source
AI summary
Disclosed are methods and systems for system call reduction. An application container may be used to encapsulate an application and to determine an operation state of the application. Based on the application state, the application container may determine one or more allowable system calls for the application. The application container may restrict access to one or more system calls excluded from the one or more allowable system calls.