Edge data correctness and correctness spread determination
AssigneeInternational Business Machines Corporation
InventorsMukundan Sundararajan, Jignesh K Karia, Shilpa Shetty
Life of this patent
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Abstract
A method, system and apparatus for providing edge data correctness and correctness spread determination, including identifying an incorrectness bias in data received at an edge server of a network through a filter with filter rules, performing corrective measures to the data according to the incorrectness bias that is identified, and learning and automatically updating the filter rules of the filter based on an extent of the incorrectness bias of the data and spread of the incorrectness bias of the data to reduce latency in future data validation of the data.
Claims18
A method for providing edge data correctness and correctness spread determination, comprising:
- identifying an incorrectness bias pattern in data received at an edge server of a network
- through a filter with filter rules,
- wherein the incorrectness bias includes spatial impurities in the data, and
- wherein spatial impurities include anomalous data that does not follow a pattern of other data in the data received,
- detecting, based on the incorrectness bias, anomalous data patterns, wherein the detected pattern indicates an identity spoofing issue;
- simulating vulnerability data assessments for the spatial impurities;
- identifying, based on the simulation, disguised data anomalies related to the spatial impurities from the incorrectness bias;
- performing corrective measures to the data according to the disguised data anomalies that are identified;
- learning and automatically updating the filter rules of the filter based on an extent of the incorrectness bias of the data and spread of the incorrectness bias of the data to reduce latency in future data validation of the data;
- and
- creating security filter rules configured to mitigate incorrectness spread and incorrectness bias, wherein the security filter rules are varied across application and usage classes, and wherein a learning method provides a variance for the security filter rules.
A system for providing edge data correctness and correctness spread determination, comprising:
- a memory storing computer instructions;
- and
- a processor configured to execute the computer instructions to:
- identify an incorrectness bias in data received at an edge server of a network through a filter with filter rules,
- wherein the incorrectness bias includes spatial impurities in the data, and
- wherein spatial impurities include anomalous data that does not follow a pattern of other data in the data received,
- detecting, based on the incorrectness bias, anomalous data patterns,
- wherein the detected pattern indicates an identity spoofing issue;
- simulate vulnerability data assessments for the spatial impurities;
- identify, based on the simulation, disguised data anomalies related to the spatial impurities from the incorrectness bias;
- perform corrective measures to the data according to the disguised data anomalies that are identified;
- learn and automatically update the filter rules of the filter based on an extent of the incorrectness bias of the data and spread of the incorrectness bias of the data to reduce latency in future data validation of the data;
- and
- create security filter rules configured to mitigate incorrectness spread and incorrectness bias, wherein the security filter rules are varied across application and usage classes, and wherein a learning method provides a variance for the security filter rules.
A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable and executable by a computer to cause the computer to perform a method for providing edge data correctness and correctness spread determination, comprising:
- identifying an incorrectness bias in data received at an edge server of a network
- through a filter with filter rules,
- wherein the incorrectness bias includes spatial impurities in the data, and
- wherein spatial impurities include anomalous data that does not follow a pattern of other data in the data received,
- detecting, based on the incorrectness bias, anomalous data patterns, wherein the detected pattern indicates an identity spoofing issue;
- simulating vulnerability data assessments for the spatial impurities;
- identifying, based on the simulation, disguised data anomalies related to the spatial impurities from the incorrectness bias;
- performing corrective measures to the data according to the disguised data anomalies that are identified;
- learning and automatically updating the filter rules of the filter based on an extent of the incorrectness bias of the data and spread of the incorrectness bias of the data to reduce latency in future data validation of the data;
- and
- creating security filter rules configured to mitigate incorrectness spread and incorrectness bias, wherein the security filter rules are varied across application and usage classes, and wherein a learning method provides a variance for the security filter rules.
Dependencies are inferred from the claim text.