Virus Trend Abnormality Detection via Statistical Residuals
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Solution Overview
Problem
Manual monitoring of virus trend abnormalities in large numbers of viruses is labor-intensive and ineffective, leading to delayed detection of new or mutated viruses.
Innovation Solution
A method and device that calculate moving average values and standardized residuals of virus hits over a predetermined period, identifying abnormal trends using preset thresholds, enabling timely detection of virus abnormalities without requiring extensive historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring of virus trends is performed by technicians, then detection accuracy may be maintained through experience, but labor consumption increases significantly and efficiency decreases when dealing with hundreds of virus types
Solution Approach 1:
The patent replaces manual mechanical monitoring by technicians with an automated computer-based system that performs statistical analysis. The system automatically collects virus hit counts, calculates moving averages, computes standardized residuals, and identifies abnormal trends without human intervention, thereby maintaining detection accuracy while dramatically improving monitoring efficiency for large numbers of virus types
Solution Approach 2:
The system enables self-service monitoring where the computer automatically performs all monitoring tasks including data collection, statistical calculation, abnormality detection, and alert generation. The automated system serves itself to monitor virus trends without requiring technician involvement, resolving the contradiction between maintaining accurate detection and improving productivity
2Ease of manufacture
If threshold-based monitoring is used to detect virus abnormalities, then implementation is simple, but new or mutated viruses cannot be detected effectively in timely manner
Solution Approach 1:
The patent changes the monitoring parameter from fixed absolute thresholds to dynamic statistical parameters (moving averages and standardized residuals). Instead of comparing virus counts against predetermined threshold values, the system calculates the mean and standard deviation from historical data, then identifies abnormalities when standardized residuals exceed a threshold. This adaptive approach maintains implementation simplicity while significantly improving detection effectiveness for new and mutated viruses that do not conform to historical patterns
Data Source
AI summary
A method and device for monitoring virus trend abnormality are provided which may enable timely and effective monitoring of computer viruses. The method may include measuring a frequency of hits of a virus being found and/or removed. The frequency may be used for calculating an M-day moving average value of the number of hits of the virus. Method may also involve calculating a standardized residual of the number of hits of the virus. When the standardized residual is larger than a first preset threshold, the time at which the virus was encounter the last may be identified as an abnormality point on a trendline of the virus.