Computer System Drift Detection Using Min-Hash Signatures
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Solution Overview
Problem
Computer system drift, caused by changes in configuration, network connections, and process trees, can lead to performance and security issues, making it challenging to detect and address potential outages or security breaches in a timely manner.
Innovation Solution
The use of hashing and min-hash techniques to monitor and detect configuration, network, and process changes across computer systems, generating compact signatures that indicate status changes, allowing for centralized detection of drift and alert generation based on velocity and threshold analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive monitoring of configuration, network, and process changes is implemented, then detection capability is improved, but data complexity and processing burden increase
Solution Approach 1:
The monitoring system segments the complex monitoring task into three distinct components: configuration monitoring, network connection monitoring, and process tree monitoring. Each component generates separate data streams that are processed independently before being integrated for comprehensive drift detection, reducing the complexity of handling all data simultaneously
Solution Approach 2:
The patent introduces min-hash signatures as an intermediary representation layer between the raw monitoring data and the drift detection analysis. Instead of directly analyzing complex configuration files, network connection data, and process trees, the system transforms them into compact min-hash signatures that preserve essential change information while dramatically reducing data complexity
2Measurement precision
If detailed monitoring of all system changes is performed, then drift detection accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts only the essential change information from comprehensive system monitoring data by using min-hash signatures. Instead of processing and analyzing all detailed configuration changes, network connection variations, and process tree modifications, the extraction mechanism captures only the critical drift indicators that matter for detection accuracy
Solution Approach 2:
The patent transforms the monitoring approach by changing the parameter representation from raw system data to min-hash signature values. This parameter transformation enables efficient comparison and change detection by working with compressed numerical representations rather than complex structured data, significantly reducing processing time while maintaining detection accuracy
3Measurement precision
If centralized monitoring of large-scale computing environments is implemented, then system-wide drift detection is improved, but computational resources increase
Solution Approach 1:
Instead of centrally collecting and processing all raw monitoring data from distributed systems, the patent implements a copying approach where each system generates local min-hash signatures that serve as compact copies of their state. These signature copies are then transmitted to the central monitoring system, enabling system-wide drift detection with minimal computational resource consumption at both distributed and central locations
Data Source
AI summary
Computer system drift can occur when a computer system or a cluster of computer systems deviates from ideal and/or desired behavior. In a server farm, for example, many different machines may be identically configured to work in conjunction with each other to provide an electronic service (serving web pages, processing electronic payment transactions, etc.). Over time, however, one or more of these systems may drift from previous behavior. Early drift detection can be important, especially in large enterprises, to avoiding costly downtime. Changes in a computer's configuration files, network connections, and/or executable processes can indicate ongoing drift, but collecting this information at scale can be difficult. By using certain hashing and min-Hash techniques, however, drift detection can be streamlined and accomplished for large scale operations. Velocity of drift may also be tracked using a decay function.