Container Image Scanning via Virtual Machine Inspection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In cloud computing environments, particularly in private and hybrid setups, customized container images may inadvertently introduce security threats and vulnerabilities when constructed from published images, as existing security scans are not comprehensive and real-time, leading to potential security risks.
Innovation Solution
A portal-based inspection engine scans virtual machine instances and container build files for security vulnerabilities, tags objects as trusted or untrusted, and initiates rectification processes through a rectification engine, utilizing machine learning for accurate classification and adaptive threat recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security scanning is performed on container images and virtual machine instances, then security reliability is improved, but scanning coverage and real-time detection capability are insufficient
Solution Approach 1:
The system segments the scanning process into multiple independent components: container image scanning, virtual machine instance scanning, and build file scanning. Each component can be executed independently and concurrently, ensuring comprehensive coverage without compromising reliability. The segmentation allows the system to tackle different scanning targets with specialized scanning mechanisms.
Solution Approach 2:
The system extends scanning from traditional single-dimension approaches to multi-dimensional coverage by simultaneously scanning container images, virtual machine instances, and associated build files. This dimensional expansion ensures that security vulnerabilities are detected across the entire containerization lifecycle, not just at a single stage.
2Difficulty of detecting and measuring
If comprehensive security scanning is performed on all container images and virtual machine instances, then security coverage is improved, but processing time and system complexity increase
Solution Approach 1:
The system performs preliminary scanning actions on container build files and images during the build process itself, before deployment. This preliminary action allows security issues to be identified early in the development lifecycle, reducing the need for extensive post-deployment scanning and overall processing time.
Solution Approach 2:
The scanning system operates continuously in the background, monitoring and scanning container images and virtual machine instances as they are created or modified. This continuous operation eliminates the need for periodic batch scanning, maintaining up-to-date security information without significant processing delays.
3Productivity
If traditional security scanning methods are used, then processing speed is maintained, but detection accuracy and threat identification capability are insufficient
Solution Approach 1:
The system introduces machine learning models as intermediary components between the scanning process and vulnerability detection. These ML models analyze scanning data to identify patterns and anomalies that traditional methods might miss, improving detection accuracy while maintaining processing speed through efficient model inference.
Solution Approach 2:
The system dynamically adjusts scanning parameters such as scan depth, analysis intensity, and detection thresholds based on the specific container image or virtual machine instance being scanned. This parameter optimization ensures high detection accuracy for complex targets while maintaining fast processing for simpler cases.
Data Source
AI summary
A technique includes accessing, by a computer, a container image that is built at least in part inside a virtual machine instance; and accessing, by the computer, an image of the virtual machine instance. Pursuant to the technique, the container image and the image of the virtual machine instance are scanned for security issues; and a result of the scanning is displayed by the computer.