Security filter for context-based data gravity wells

• US 8,856,946 B2
• Filed: 01/31/2013
• Issued: 10/07/2014
• Est. Priority Date: 01/31/2013
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Adams, Samuel S., Friedlander, Robert R., Kraemer, James R., Linton, Jeb R.
• Primary Examiner(s): Shaw, Peter
• Assistant Examiner(s): Shayanfar, Ali
• Application Number:
  US13/755,623
• Publication Number:
  US 20140215633A1
• Time in Patent Office: 614 Days
• Field of Search: 726/26, 726/29, 726/2, 705/44, 705/76
• US Class Current: 726/26
• CPC Class Codes: H04L 63/1408 by monitoring network traffic monitoring network traffic per se H04L43/00

Abstract

A processor-implemented method, system, and/or computer program product defines multiple security-enabled context-based data gravity wells on a security-enabled context-based data gravity wells membrane. Non-contextual data objects are associated with context objects to define synthetic context-based objects. The synthetic context-based objects are associated with one or more security objects to generate security-enabled synthetic context-based objects, which are parsed into an n-tuple that includes a pointer to one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, probability that the security object has been associated with a correct synthetic context-based object, and a weighting factor of importance of the security-enabled synthetic context-based object. A virtual mass of each parsed security-enabled synthetic context-based object is calculated, in order to define a shape of multiple security-enabled context-based data gravity wells that are created when security-enabled synthetic context-based objects are pulled in.

Claims

1. A processor-implemented method of defining multiple security-enabled context-based data gravity wells on a security-enabled context-based data gravity wells membrane, the processor-implemented method comprising:

receiving a data stream of non-contextual data objects, wherein each of the non-contextual data objects ambiguously relates to multiple subject-matters;

associating, by one or more hardware processors, one of the non-contextual data objects with a context object to define a synthetic context-based object, wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of said one of the non-contextual data objects;

associating, by one or more hardware processors, the synthetic context-based object with a security object to generate a security-enabled synthetic context-based object, wherein the security object describes a circumstance that describes an environment in which an event is occurring;

parsing, by one or more hardware processors, the security-enabled synthetic context-based object into an n-tuple, wherein the n-tuple comprises a pointer to said one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, a weighting factor of importance of the security-enabled synthetic context-based object, and a probability that the security object has been associated with a correct synthetic context-based object;

calculating, by one or more hardware processors, a virtual mass of the parsed security-enabled synthetic context-based object, wherein the virtual mass of the parsed security-enabled synthetic context-based object is derived from a formula of;

(P(C)+P(S))×

Wt(S),

where P(C) is the probability that the non-contextual data object has been associated with the correct context object, wherein P(S) is the probability that the security object has been associated with the correct synthetic context-based object, and where Wt(S) is the weighting factor of importance of the security-enabled synthetic context-based object;

creating, by one or more hardware processors, multiple security-enabled context-based data gravity well frameworks on a security-enabled context-based data gravity wells membrane, wherein each of the multiple security-enabled context-based data gravity well frameworks comprises at least one non-contextual data object, at least one context object, and at least one security object, and wherein the security-enabled context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple security-enabled context-based data gravity wells;

transmitting multiple parsed security-enabled synthetic context-based objects to the security-enabled context-based data gravity wells membrane;

defining, by said one or more hardware processors, multiple security-enabled context-based data gravity wells according to the virtual mass of multiple parsed security-enabled synthetic context-based objects that are pulled into each of the security-enabled context-based data gravity well frameworks, wherein each of the multiple parsed security-enabled synthetic context-based objects is pulled into a particular security-enabled context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object, said at least one context object, and said at least one security object in said particular security-enabled context-based data gravity well; and

in response to an unmatched parsed security-enabled synthetic context-based object failing to be pulled into any of the security-enabled context-based data gravity wells, trapping, by one or more hardware processors, said unmatched parsed security-enabled synthetic context-based object in an unmatched parsed security-enabled synthetic context-based object trap.

2. The processor-implemented method of claim 1, further comprising:

processing, by one or more hardware processors, the unmatched parsed security-enabled synthetic context-based object to generate an alert, wherein the alert indicates that the unmatched parsed security-enabled synthetic context-based object represents a financially fraudulent event.

3. The processor-implemented method of claim 1, further comprising:

graphically displaying, by one or more hardware processors, the multiple security-enabled context-based data gravity wells according to a combined virtual mass of the multiple parsed security-enabled synthetic context-based objects, wherein a first security-enabled context-based data gravity well holds a more virtually massive combination of parsed security-enabled synthetic context-based objects than a second security-enabled context-based data gravity well, and wherein the first security-enabled context-based data gravity well extends farther away from the security-enabled context-based data gravity wells membrane than the second security-enabled context-based data gravity well.

4. The processor-implemented method of claim 1, further comprising:

determining, by one or more hardware processors, a likelihood that a particular security-enabled synthetic context-based object is pulled into an appropriate security-enabled context-based data gravity well according to a Bayesian probability formula of;

5. The processor-implemented method of claim 1, wherein the weighting factor of importance of the security-enabled synthetic context-based object is based on how important the security-enabled synthetic context-based object is to a particular project.

6. The processor-implemented method of claim 1, wherein the correct context object is a context object that defines the specific subject-matter of a particular project.

7. The processor-implemented method of claim 1, further comprising:

graphically representing, by one or more hardware processors, said at least one context object and said at least one security object on a wall of said particular security-enabled context-based data gravity well.

8. The processor-implemented method of claim 1, further comprising:

determining, by one or more hardware processors, an age of each of the multiple parsed security-enabled synthetic context-based objects that have been pulled into the particular security-enabled context-based data gravity well; and

removing, by one or more hardware processors, from the particular security-enabled context-based data gravity well any parsed security-enabled synthetic context-based object that is older than a predetermined age.

9. A computer program product for defining multiple security-enabled context-based data gravity wells on a security-enabled context-based data gravity wells membrane, the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by one or more hardware processors to perform a method comprising:

receiving a data stream of non-contextual data objects, wherein each of the non-contextual data objects ambiguously relates to multiple subject-matters;

associating one of the non-contextual data objects with a context object to define a synthetic context-based object, wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of said one of the non-contextual data objects;

associating the synthetic context-based object with a security object to generate a security-enabled synthetic context-based object, wherein the security object describes a circumstance that describes an environment in which an event is occurring;

parsing the security-enabled synthetic context-based object into an n-tuple, wherein the n-tuple comprises a pointer to said one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, a weighting factor of importance of the security-enabled synthetic context-based object, and a probability that the security object has been associated with a correct synthetic context-based object;

calculating a virtual mass of the parsed security-enabled synthetic context-based object, wherein the virtual mass of the parsed security-enabled synthetic context-based object is derived from a formula of;

(P(C)+P(S))×

Wt(S),

where P(C) is the probability that the non-contextual data object has been associated with the correct context object, wherein P(S) is the probability that the security object has been associated with the correct synthetic context-based object, and where Wt(S) is the weighting factor of importance of the security-enabled synthetic context-based object;

creating multiple security-enabled context-based data gravity well frameworks on a security-enabled context-based data gravity wells membrane, wherein each of the multiple security-enabled context-based data gravity well frameworks comprises at least one non-contextual data object, at least one context object, and at least one security object, and wherein the security-enabled context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple security-enabled context-based data gravity wells;

transmitting multiple parsed security-enabled synthetic context-based objects to the security-enabled context-based data gravity wells membrane;

defining multiple security-enabled context-based data gravity wells according to the virtual mass of multiple parsed security-enabled synthetic context-based objects that are pulled into each of the security-enabled context-based data gravity well frameworks, wherein each of the multiple parsed security-enabled synthetic context-based objects is pulled into a particular security-enabled context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object, said at least one context object, and said at least one security object in said particular security-enabled context-based data gravity well; and

in response to an unmatched parsed security-enabled synthetic context-based object failing to be pulled into any of the security-enabled context-based data gravity wells, trapping said unmatched parsed security-enabled synthetic context-based object in an unmatched parsed security-enabled synthetic context-based object trap.

10. The computer program product of claim 9, further comprising program code that is readable and executable by said one or more hardware processors to:

process the unmatched parsed security-enabled synthetic context-based object to generate an alert, wherein the alert indicates that the unmatched parsed security-enabled synthetic context-based object represents a financially fraudulent event.

11. The computer program product of claim 9, further comprising program code that is readable and executable by said one or more hardware processors to:

graphically display the multiple security-enabled context-based data gravity wells according to a combined virtual mass of the multiple parsed security-enabled synthetic context-based objects, wherein a first security-enabled context-based data gravity well holds a more virtually massive combination of parsed security-enabled synthetic context-based objects than a second security-enabled context-based data gravity well, and wherein the first security-enabled context-based data gravity well extends farther away from the security-enabled context-based data gravity wells membrane than the second security-enabled context-based data gravity well.

12. The computer program product of claim 9, further comprising program code that is readable and executable by said one or more hardware processors to:

determine a likelihood that a particular security-enabled synthetic context-based object is pulled into an appropriate security-enabled context-based data gravity well according to a Bayesian probability formula of;

13. The computer program product of claim 9, wherein the weighting factor of importance of the security-enabled synthetic context-based object is based on how important the security-enabled synthetic context-based object is to a particular project.

14. The computer program product of claim 9, wherein the correct context object is a context object that defines the specific subject-matter of a particular project.

15. The computer program product of claim 9, further comprising program code that is readable and executable by said one or more hardware processors to:

graphically represent said at least one context object and said at least one security object on a wall of said particular security-enabled context-based data gravity well.

16. The computer program product of claim 9, further comprising program code that is readable and executable by said one or more hardware processors to:

determine an age of each of the multiple parsed security-enabled synthetic context-based objects that have been pulled into the particular security-enabled context-based data gravity well; and

remove from the particular security-enabled context-based data gravity well any parsed security-enabled synthetic context-based object that is older than a predetermined age.

18. The computer system of claim 16, further comprising:

tenth program instructions to process the unmatched parsed security-enabled synthetic context-based object to generate an alert, wherein the alert indicates that the unmatched parsed security-enabled synthetic context-based object represents a financially fraudulent event; and

wherein

the tenth program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory.

19. The computer system of claim 16, further comprising:

tenth program instructions to graphically display the multiple security-enabled context-based data gravity wells according to a combined virtual mass of the multiple parsed security-enabled synthetic context-based objects, wherein a first security-enabled context-based data gravity well holds a more virtually massive combination of parsed security-enabled synthetic context-based objects than a second security-enabled context-based data gravity well, and wherein the first security-enabled context-based data gravity well extends farther away from the security-enabled context-based data gravity wells membrane than the second security-enabled context-based data gravity well; and

wherein

the tenth program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory.

20. The computer system of claim 16, further comprising:

tenth program instructions to determine a likelihood that a particular security-enabled synthetic context-based object is pulled into an appropriate security-enabled context-based data gravity well according to a Bayesian probability formula of;

17. A computer system comprising:

a processor, a computer readable memory, and a computer readable storage medium;

first program instructions to receive a data stream of non-contextual data objects, wherein each of the non-contextual data objects ambiguously relates to multiple subject-matters;

second program instructions to associate one of the non-contextual data objects with a context object to define a synthetic context-based object, wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of said one of the non-contextual data objects;

third program instructions to associate the synthetic context-based object with a security object to generate a security-enabled synthetic context-based object, wherein the security object describes a circumstance that describes an environment in which an event is occurring;

fourth program instructions to parse the security-enabled synthetic context-based object into an n-tuple, wherein the n-tuple comprises a pointer to said one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, a weighting factor of importance of the security-enabled synthetic context-based object, and a probability that the security object has been associated with a correct synthetic context-based object;

fifth program instructions to calculate a virtual mass of the parsed security-enabled synthetic context-based object, wherein the virtual mass of the parsed security-enabled synthetic context-based object is derived from a formula of;

(P(C)+P(S))×

Wt(S),

where P(C) is the probability that the non-contextual data object has been associated with the correct context object, wherein P(S) is the probability that the security object has been associated with the correct synthetic context-based object, and where Wt(S) is the weighting factor of importance of the security-enabled synthetic context-based object;

sixth program instructions to create multiple security-enabled context-based data gravity well frameworks on a security-enabled context-based data gravity wells membrane, wherein each of the multiple security-enabled context-based data gravity well frameworks comprises at least one non-contextual data object, at least one context object, and at least one security object, and wherein the security-enabled context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple security-enabled context-based data gravity wells;

seventh program instructions to transmit multiple parsed security-enabled synthetic context-based objects to the security-enabled context-based data gravity wells membrane;

eighth program instructions to define multiple security-enabled context-based data gravity wells according to the virtual mass of multiple parsed security-enabled synthetic context-based objects that are pulled into each of the security-enabled context-based data gravity well frameworks, wherein each of the multiple parsed security-enabled synthetic context-based objects is pulled into a particular security-enabled context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object, said at least one context object, and said at least one security object in said particular security-enabled context-based data gravity well; and

ninth program instructions to, in response to an unmatched parsed security-enabled synthetic context-based object failing to be pulled into any of the security-enabled context-based data gravity wells, trap said unmatched parsed security-enabled synthetic context-based object in an unmatched parsed security-enabled synthetic context-based object trap; and

wherein

the first, second, third, fourth, fifth, sixth, seventh, eighth, and ninth program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory.