Retrospective Memory Analysis for Execution Environment Issue Detection
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Solution Overview
Problem
Existing methods struggle to detect and manage unknown malware and other issues in execution environments due to the lag between issue manifestation and detectability, as well as the difficulty in identifying the duration and scope of such issues, especially when they evade detection by lacking associated signatures or metadata.
Innovation Solution
A retrospective memory analysis system that captures and stores historical memory images of execution environments, allowing for analysis using a current set of known issues to determine when an issue was present or resolved, even if it was not known at the time of memory capture, using differential encoding and compression techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If contemporaneous issue identification is used, then known issues can be detected and resolved timely, but unknown issues that evade detection cannot be identified
Solution Approach 1:
The system performs preliminary actions by capturing and storing memory images at regular intervals before issues manifest or evolve. These historical memory images serve as a foundation for retrospective analysis, allowing detection of unknown issues that were not identifiable at the time of capture but can be detected now using updated detection criteria.
Solution Approach 2:
The system implements feedback by continuously updating detection criteria with newly identified issues and using historical memory images to retrospectively identify issues that were present but undetected. This feedback loop enables the system to learn from past detections and improve future detection capabilities.
2Difficulty of detecting and measuring
If memory images are captured and stored for retrospective analysis, then unknown issues can be identified, but storage requirements and system complexity increase
Solution Approach 1:
The system segments memory images into manageable units and organizes them in a structured manner with metadata indicating capture time, environment details, and associated processes. This segmentation enables efficient storage, retrieval, and analysis while reducing the complexity of managing large volumes of historical data.
Solution Approach 2:
The system extracts and stores only the essential memory images at critical intervals rather than continuously monitoring every possible state. By selecting representative memory snapshots, the system reduces storage requirements while maintaining sufficient data for retrospective analysis of unknown issues.
3Difficulty of detecting and measuring
If historical memory images are analyzed with current detection criteria, then retrospective identification of issues is enabled, but detection speed and responsiveness decrease
Solution Approach 1:
The system prepares historical memory images in advance for retrospective analysis, organizing them with associated metadata and detection criteria. This preliminary preparation enables rapid analysis when needed, as the data is already structured and ready for evaluation against current detection criteria without requiring real-time processing of raw memory data.
Data Source
AI summary
Aspects of the present disclosure relate to retrospective memory analysis. In examples, a historical archive of memory images for an execution environment is maintained. A historical memory image of the historical archive may be evaluated according to a current set of known issues, rather than issues that were known at the time of the memory capture. Accordingly, it may be possible to determine when the execution environment was last in a good environment condition. As another example, it may be possible to determine whether a now-known issue has since been resolved (e.g., such that the issue would not be identified in the current execution environment). Thus, as compared to contemporaneous issue identification techniques, aspects of the present disclosure may be applied to any number of execution environments to enable retrospective identification of now-known issues that were, at least at the time of a memory capture, not known.