VM Migration Malware Detection via Execution Profile Comparison
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Solution Overview
Problem
The challenge in migrating virtual machines is the potential transfer and propagation of malicious software or malware, such as viruses, worms, and trojans, which can compromise the new virtualized computing environment.
Innovation Solution
A malware detection system that generates profiles of execution characteristics for virtual machines before and after migration, using a hypervisor or virtual machine monitor, to identify potential malware by comparing these profiles and detecting differences that indicate malicious activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual machine migration is performed without malware detection, then migration speed and simplicity are improved, but security reliability deteriorates due to potential malware transfer
Solution Approach 1:
The system performs preliminary malware detection by comparing VM execution profiles before migration occurs. The baseline profile is captured during initial VM deployment, and this profile is used to detect malware before the migration process begins, preventing malware propagation while maintaining efficient migration.
Solution Approach 2:
The invention introduces an intermediary malware detection system that acts as a mediator between the source and target environments. The detection system compares execution profiles and identifies malware without requiring direct interaction between the migrating VM and the target environment, enabling secure migration verification.
2Reliability
If execution profile monitoring is implemented for malware detection, then security reliability is improved, but system complexity increases due to additional monitoring overhead
Solution Approach 1:
The VM itself generates the execution profile data through its normal operation within the virtualized environment. The monitoring system captures execution characteristics that are naturally produced by the VM's operation, reducing the need for additional complex monitoring infrastructure while maintaining detection accuracy.
Solution Approach 2:
The execution profile serves multiple functions: it characterizes normal VM behavior for performance optimization, provides baseline data for security anomaly detection, and enables migration verification. This multi-functionality reduces system complexity by using a single data collection mechanism for multiple purposes.
3Measurement precision
If detailed execution characteristics are monitored, then malware detection precision is improved, but data processing volume increases
Solution Approach 1:
The system extracts only the most relevant execution characteristics from the complete VM operation data. By selecting specific metrics that are most indicative of malware behavior (such as CPU usage patterns, memory access patterns, and system call frequencies), the system maintains high detection precision while minimizing data processing volume.
Solution Approach 2:
Different execution characteristics are monitored with different levels of detail based on their diagnostic value. Critical parameters that are most indicative of malware (such as unusual system calls or memory access patterns) are monitored with higher precision, while less critical parameters are monitored at lower resolution, optimizing the balance between detection precision and data volume.
Data Source
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AI summary
A malware detection system to detect malware in a virtual machine (VM), the system comprising: a profile generator adapted to generate a profile of a deployment of the VM, the profile including execution characteristics of the deployment; a VM package generator to generate a VM package including: a VM descriptor describing a particular deployment of the VM; and an image of the particular deployment, the image including a representation of data stored for the particular deployment of the VM; and a malware identifier adapted to identify malware in a deployment of the VM responsive to the identification of a difference between profiles of multiple different deployments of the VM.