Container Malware Detection Using System Call Deviation Analysis
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Solution Overview
Problem
Existing malware detection methods in containerized environments face challenges such as high false positive rates and inability to adapt to changing behaviors, with static analysis failing to detect new threats and dynamic analysis being inefficient.
Innovation Solution
A method involving monitoring system calls, comparing them to prior behavior, isolating deviations, and classifying them using machine learning models to reduce false positives by vectorizing features and isolating processes within containerized systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static analysis is used for malware detection, then detection simplicity is maintained, but detection accuracy deteriorates due to inability to detect new threats
Solution Approach 1:
The system transitions from static analysis to dynamic analysis by monitoring system calls and container behavior in real-time. The behavior model continuously learns from container operations, adapting to new threats while maintaining automated detection simplicity through machine learning algorithms.
Solution Approach 2:
The patent replaces traditional mechanical static analysis methods with machine learning-based dynamic analysis. The system uses automated machine learning models to analyze container behavior patterns, substituting manual rule-based detection with adaptive algorithmic detection that improves accuracy while maintaining automation.
2Measurement precision
If dynamic analysis is used for malware detection, then detection accuracy is improved, but processing efficiency deteriorates
Solution Approach 1:
The system segments the analysis process by focusing monitoring on specific container behaviors and system calls rather than analyzing all operations. The behavior model learns to identify relevant patterns, dividing the complex detection task into manageable segments that improve processing efficiency while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary action by pre-training behavior models on normal container operations before actual malware detection. The models learn expected behavior patterns in advance, enabling faster real-time detection by comparing against established baselines rather than performing full analysis on every operation.
3Reliability
If comprehensive monitoring is performed on all container operations, then detection coverage is improved, but system complexity increases
Solution Approach 1:
The system extracts only the most relevant features from container operations for analysis. The behavior model identifies and focuses on critical system calls and behavioral patterns that indicate malware, discarding redundant information. This extraction reduces system complexity by processing only essential data while maintaining comprehensive detection coverage.
Solution Approach 2:
The system applies local quality by using different analysis approaches for different container operations. Rather than uniform comprehensive monitoring, the system adapts its monitoring intensity and methods based on the specific operation type, reducing overall system complexity while maintaining detection coverage where it matters most.
4Measurement precision
If behavior modeling is used to reduce false positives, then detection precision is improved, but computational resources increase
Solution Approach 1:
The system creates simplified copies or representations of normal container behavior through behavior models. Rather than performing full complex analysis on every operation, the system uses learned behavior patterns as proxies, reducing computational resource requirements while maintaining the precision needed to reduce false positives.
Data Source
AI summary
A method, computer system, and a computer program for malware detection in containerized environments are provided. The method may include monitoring operation of a container image by receiving a plurality of system calls performed during the operation of the container image. The method further includes comparing the plurality of system calls to prior container behavior associated with previous operation of a prior container image, and identifying a deviation from the prior container behavior. The method further includes isolating a subset of the plurality of system calls and classifying the subset to a malware class of a plurality of malware classes.