IP Library Granted Patent US 10,212,184
Granted Patent B2
US 10,212,184 · App. 15/336,307 · Granted Feb 19, 2019

Method for the continuous calculation of a cyber security risk index

Inventors: Matthew S. Sweeney (Ontario, NY); Benjamin B. Pokines (Rome, NY)
Assignee: OPAQ Networks, Inc.
H04L63/1433G06F17/30321G06F21/552G06F21/554H04L63/1425
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Quick Facts
Patent No.
US 10,212,184
App. No.
15/336,307
Filed
Oct 27, 2016
Granted
Feb 19, 2019
Kind
B2
Examiner
ZEE, EDWARD
Art Unit
2435
USPC
726/25
Abstract

A method for assessing a cyber security risk, the method comprising the steps of: obtaining cyber security precursor information from a plurality of sources, wherein the cyber security precursor information can be obtained from one or more online or offline sources; normalizing the obtained cyber security precursor information to a common information model; generating, from the normalized cyber security precursor information, one or more events; producing, from the one or more generated events, one or more facts; calculating a plurality of risk indicators from the one or more facts; normalizing the plurality of risk indicators to a common model; calculating, using the normalized plurality of risk indicators, one or more cyber risk index component scores; and calculating, using the one or more cyber risk index component scores, a cyber risk indicator index.

Claims (64)

1. A method for assessing a cyber security risk, the method comprising the steps of:

obtaining cyber security precursor information from a plurality of sensors, wherein the cyber security precursor information is obtained from one or more online or offline sources;

normalizing the obtained cyber security precursor information from each sensor to a common information model based on a data source type identified for each sensor;

generating, from the normalized cyber security precursor information, a plurality of events;

computing, from the plurality of generated events, a plurality of facts, each fact comprising a reference to a set of events or facts that contributed to computing the fact;

calculating a plurality of risk indicators from the plurality of facts, each risk indicator calculated based on a time-series analysis of a type of the fact;

normalizing the plurality of risk indicators to a common model based on normalizing a plurality of facts used to calculate each risk indicator according to a data source type shared across events or analytic facts used to calculate each risk indicator;

calculating, using the plurality of normalized facts for each risk indicator, a plurality of cyber risk index component scores for each data source type; and

calculating, using the plurality cyber risk index component scores, a cyber risk indicator index.

2. The method of claim 1 , further comprising the step of storing the plurality of generated events in an event database.

3. The method of claim 1 , further comprising the step of storing the plurality of generated facts in a fact database.

4. The method of claim 1 , further comprising the step of storing the plurality of generated risk indicators in a risk indicator database.

5. The method of claim 1 , wherein the step of computing the plurality of facts comprises one or more of performing a mathematical analysis, pattern detection, anomaly detection, and rule-based assessment of the plurality of events.

6. The method of claim 1 , wherein the cyber risk indicator index comprises a score between 0 and 100.

7. The method of claim 1 , further comprising the step of providing the cyber risk indicator index to a user.

8. The method of claim 7 , wherein the step of providing the cyber risk indicator index to a user comprises a user interface.

9. The method of claim 8 , wherein the user interface comprises a graph of cyber risk indicator index over time.

10. The method of claim 1 , further comprising the step of comparing the cyber risk indicator index to a pre-determined threshold.

11. The method of claim 10 , further comprising the step of notifying a user if the cyber risk indicator index exceeds the pre-determined threshold.

12. The method of claim 10 , further comprising the step of initiating an automated action if the cyber risk indicator index exceeds the pre-determined threshold.

13. The method of claim 1 , wherein normalizing the obtained cyber security precursor information from each sensor comprises:

weighting the cyber security precursor information from each sensor based on a percentage of network assets that are observed by each sensor and based on an operational impact of the observed network assets for each sensor.

14. A computerized system configured to assess a cyber security risk, the system comprising:

a plurality of sensors, the plurality of sensors configured to obtain cyber security precursor information;

an event database configured to store one or more events;

a fact database configured to store one or more facts; and

a processor programmed to perform the steps of:

receiving the cyber security precursor information from the plurality of sensors;

normalizing the obtained cyber security precursor information from each sensor to a common information model based on a data source type identified for each sensor;

generating, from the normalized cyber security precursor information, the plurality of events;

storing the plurality of generated events in the event database;

computing, from the plurality of generated events, a plurality of facts, each fact comprising a reference to a set of events or facts that contributed to computing the fact;

storing the plurality of facts in the facts database;

calculating a plurality of risk indicators from the plurality of facts, each risk indicator calculated based on a time-series analysis of a type of the fact;

normalizing the plurality of risk indicators to a common model based on normalizing a plurality of facts used to calculate each risk indicator according to a data source type shared across events or analytic facts used to calculate each risk indicator;

calculating, using the plurality of normalized facts for each risk indicator, a plurality of cyber risk index component scores for each data source type; and

calculating, using the plurality cyber risk index component scores, a cyber risk indicator index.

15. The system of claim 14 , wherein the processor is configured to compute the plurality of facts using one or more of a mathematical analysis, pattern detection, anomaly detection, and rule-based assessment of the plurality of events.

16. The system of claim 14 , wherein the cyber risk indicator index comprises a score between 0 and 100.

17. The system of claim 14 , wherein the processor is further configured to provide the cyber risk indicator index to a user.

18. The system of claim 14 , further comprising a user interface.

19. The system of claim 14 , wherein the processor is further configured to compare the cyber risk indicator index to a pre-determined threshold.

20. The system of claim 19 , wherein the processor is further configured to notify a user if the cyber risk indicator index exceeds the pre-determined threshold.

21. The system of claim 19 , wherein the processor is further configured to initiate an automated action if the cyber risk indicator index exceeds the pre-determined threshold.

22. A computer system configured to assess a cyber security risk, the computer system comprising:

a non-transitory computer-readable storage medium configured to store data collected by the computer system and comprising computer-executable instructions;

a processor programmed to execute the computer-executable instructions resulting in the computer system performing the steps of:

receiving cyber security precursor information from a plurality of sensors;

normalizing the obtained cyber security precursor information from each sensor to a common information model based on a data source type identified for each sensor;

generating, from the normalized cyber security precursor information, a plurality of events;

storing the plurality of generated events in the non-transitory computer-readable storage medium;

computing, from the plurality of generated events, a plurality of facts, each fact comprising a reference to a set of events or facts that contributed to computing the fact;

storing the plurality of facts in the non-transitory computer-readable storage medium;

calculating a plurality of risk indicators from the plurality of facts, each risk indicator calculated based on a time-series analysis of a type of the fact;

normalizing the plurality of risk indicators to a common model based on normalizing a plurality of facts used to calculate each risk indicator according to a data source type shared across events or analytic facts used to calculate each risk indicator;

calculating, using the plurality of normalized facts for each risk indicator, a plurality of cyber risk index component scores for each data source type; and

calculating, using the plurality cyber risk index component scores, a cyber risk indicator index.

23. The computer system of claim 22 , wherein the processor is further configured to compute the plurality of facts using one or more of a mathematical analysis, pattern detection, anomaly detection, and rule-based assessment of the plurality of events.

24. The computer system of claim 22 , wherein the cyber risk indicator index comprises a score between 0 and 100.

25. The computer system of claim 22 , wherein the processor is further configured to provide the cyber risk indicator index to a user.

26. The computer system of claim 22 , further comprising a user interface.

27. The computer system of claim 22 , wherein the processor is further configured to compare the cyber risk indicator index to a pre-determined threshold.

28. The computer system of claim 27 , wherein the processor is further configured to notify a user if the cyber risk indicator index exceeds the pre-determined threshold.

29. The computer system of claim 27 , wherein the processor is further configured to initiate an automated action if the cyber risk indicator index exceeds the pre-determined threshold.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 27, 2020
From: OPAQ NETWORKS, INC.
To: FORTINET, INC.
Reel/Frame 053613/0746 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 29, 2018
From: FOURV SYSTEMS, LLC
To: OPAQ NETWORKS, INC.
Reel/Frame 046242/0293 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 13, 2018
From: SRC, INC.
To: FOURV SYSTEMS, LLC
Reel/Frame 045182/0170 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 16, 2016
From: SWEENEY, MATTHEW S.; POKINES, BENJAMIN B.
To: SRC, INC.
Reel/Frame 040639/0544 →
Continuity (1)
Related Publication 20180124091A1 · May 3, 2018
Cited By (5)
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