Edge data correctness and correctness spread determination
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.
1. 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.
2. The method according to claim 1 , wherein the identifying of the incorrectness bias in the data received is identified at the edge server and subsequent stages of the network through the filter including a security filter.
3. The method according to claim 1 , wherein the identifying of the incorrectness bias of the data is at a lowest level of data ingestion into the edge server, and
wherein the corrective measures include flagging the data to adjust value of the data by remeasuring by a sensor that initially transmits the data to the edge server.
4. The method according to claim 3 , wherein the flagging of the data includes when anomalous data patterns across transactions are detected.
5. The method according to claim 1 , wherein the identifying further includes temporal simulation and correlation of the data received by the edge server to detect embedded data threats.
6. The method according to claim 1 implemented in a cloud environment, wherein the incorrectness bias spread and the incorrectness bias with the filter including a security filter in the edge server and subsequent stages of the network are used to identify incorrectness of the data to validate data integrity.
7. 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.
8. The system according to claim 7 , wherein the identifying of the incorrectness bias in the data received is identified at the edge server and subsequent stages of the network through the filter including a security filter.
9. The system according to claim 7 , wherein the identifying of the incorrectness bias of the data is at a lowest level of data ingestion into the edge server, and
wherein the corrective measures include flagging the data to adjust value of the data by remeasuring by a sensor that initially transmits the data to the edge server.
10. The system according to claim 9 , wherein the flagging of the data includes when anomalous data patterns across transactions are detected.
11. The system according to claim 7 , wherein the identifying further includes temporal simulation and correlation of the data received by the edge server to detect embedded data threats.
12. The system according to claim 7 , wherein the incorrectness bias spread and the incorrectness bias with the filter including a security filter in the edge server and subsequent stages of the network are used to identify incorrectness of the data to validate data integrity.
13. 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.
14. The computer program product according to claim 13 ,
wherein the identifying of the incorrectness bias in the data received is identified at the edge server and subsequent stages of the network through the filter including a security filter.
15. The computer program product according to claim 13 , wherein the identifying of the incorrectness bias of the data is at a lowest level of data ingestion into the edge server, and
wherein the corrective measures include flagging the data to adjust value of the data by remeasuring by a sensor that initially transmits the data to the edge server.
16. The computer program product according to claim 13 , wherein flagging of the data includes when anomalous data patterns across transactions are detected.
17. The computer program product according to claim 13 , wherein the identifying further includes temporal simulation and correlation of the data received by the edge server to detect embedded data threats.
18. The computer program product according to claim 13 , wherein the incorrectness bias spread and the incorrectness bias with the filter including a security filter in the edge server and subsequent stages of the network are used to identify incorrectness of the data to validate data integrity.