Application Load Cycle Anomaly Detection via Parameter Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in identifying and detecting anomalies between load cycles of an application due to the lack of specific information being available or stored separately, leading to delayed identification of issues.
Innovation Solution
A method is introduced where load, execution, and unload parameters from one or more load cycles of an application are retrieved and compared to identify anomalies, using a database or a distributed ledger/blockchain to store and analyze these parameters, enabling real-time or semi-real-time anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If parameters are stored in separate records without centralized tracking, then storage simplicity is maintained, but anomaly detection time increases
Solution Approach 1:
The patent merges load parameters, execution parameters, and unload parameters into a unified tracking system that compares parameters across consecutive load cycles. This consolidation enables centralized anomaly detection by having all necessary parameters available in one place for comparison, directly reducing anomaly detection time while maintaining manageable system complexity through structured parameter organization.
Solution Approach 2:
The system performs preliminary storage and organization of load, execution, and unload parameters during each load cycle before anomaly detection is needed. By pre-capturing and storing these parameters in a structured format during normal operation, the system prepares the data necessary for rapid anomaly detection when comparing consecutive load cycles, thus reducing detection time without requiring complex real-time analysis infrastructure.
2Measurement precision
If specific parameter information is not stored separately, then storage simplicity is maintained, but anomaly identification accuracy decreases
Solution Approach 1:
The patent segments parameter tracking into three distinct categories: load parameters (captured when application is loaded), execution parameters (captured during application execution), and unload parameters (captured when application is unloaded). This segmentation allows each parameter type to be stored and tracked separately with appropriate detail, improving anomaly identification accuracy by ensuring specific information is captured and made available for comparison across load cycles while maintaining organized storage structure.
3Reliability
If comprehensive parameter tracking is implemented across load cycles, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The system implements feedback by comparing parameters from consecutive load cycles and using the results to identify anomalies. The comparison mechanism feeds back information about parameter changes between cycles, enabling the system to detect deviations that indicate malicious behavior or misconfiguration. This feedback loop improves anomaly detection capability while keeping system complexity manageable by focusing comparisons on specific parameter categories (load, execution, unload) rather than all possible system parameters.
Data Source
AI summary
A first load cycle of an application is determined to have been completed. A load cycle is where the application has been loaded, executed, and then unloaded. One or more of first load parameter associated with the first load cycle of the application, a first execution parameter associated with the first load cycle of the application, and a first unload parameter associated with the first load cycle of the application are retrieved and compared to one or more of a second load parameter associated with a second load cycle of the application, a second execution parameter associated with the second load cycle of the application, and a second unload parameter associated with the second load cycle of the application. The comparison can then be used to identify anomalies between load cycles of the application.