System and method for integrated and synchronized planning and response to defeat disparate threats over the threat kill chain with combined cyber, electronic warfare and kinetic effects

Patent metadata

Field	Value
Publication number	US-10641585-B2
Application number	US-201715445095-A
Country	US
Kind code	B2
Filing date	Feb 28, 2017
Priority date	Mar 8, 2016
Publication date	May 5, 2020
Grant date	May 5, 2020

Abstract

An automated system and method provides for planning and response to disparate threats with combined kinetic (e.g., missile interceptor) and non-kinetic (e.g., cyber and electronic warfare) effects. The threat and effects inputs and the results of analysis tools that individually apply a single effect type to negate a threat vulnerability are normalized to a form and format ingestible by a Stochastic Math Model (SMM). The normalized inputs reflect the success/failure of the effect versus a threat vulnerability as a score, not a probability. The normalized results decompose a probability of defeat (Pdefeat) for an individual effect as a set of hierarchical parameters represented as probabilities. The SMM and analysis tools are iteratively run on the normalized inputs and results data, including either organically or synthetically injected variability of the data, to compute and visualize a Pdefeat with an associated confidence interval (CI) for each effect and the combined effects at each stage of the threat kill chain.

First claim

We claim:

1. A method of analyzing kinetic and non-kinetic effects for negating kinetic and non-kinetic threats, the method comprising:
defining a threat kill chain for one or more kinetic or non-kinetic threats;
generating a scenario that combines kinetic and non-kinetic effects to engage the one or more kinetic or non-kinetic threats at different stages of the threat kill chain;
normalizing input data for the one or more threats and the kinetic and non-kinetic effects to an input format ingestible by a stochastic math model (SMM);
applying effect-specific analysis tools to one or more of the kinetic and non-kinetic effects in the scenario to individually negate one of the one or more threats and generate results data for each said effect-threat pair;
normalizing the results data for each said effect to a set of hierarchical parameters ingestible by the SMM;
iteratively running the SMM on the normalized input and results data to compute a probability of defeat (Pdefeat) with an associated confidence interval (CI) for each said effect and the combined effects at each stage of the threat kill chain; and
displaying the Pdefeat and CIs for each said effect and the combined effects at each stage of the threat kill chain.

2. The method of claim 1 , wherein the step of generating the scenario pairs each effect with a threat vulnerability in the threat kill chain.

3. The method of claim 1 , further comprising evaluating one or more constraints selected from a policy on the use of effects, effect cost, effect availability and an effect technology readiness level (TRL) for the scenario.

4. The method of claim 3 , further comprising using the one or more constraints to either pre-screen the scenario prior to running the SMM or to display the one or more constraints.

5. The method of claim 1 , further comprising different scenarios for different threats, wherein a scenario is generated for an active threat by selecting multiple existing scenarios modeled for threats and synthesizing the selected scenarios based on metadata for the active threat and existing scenarios.

6. The method of claim 1 , wherein the steps of normalizing the input data and the results data comprises applying a rules-based module to map the input data or results data to a delimited file format ingestible by the SMM.

7. The method of claim 1 , wherein each parameter in the set of hierarchical parameters is represented as a probability with an associated confidence interval.

8. The method of claim 1 , wherein a discrete event simulator (DES) is configured to implement the SMM and perform a Monte Carlo simulation on organically or synthetically varied input and results data to generate the Pdefeat and confidence intervals.

9. The method of claim 1 , wherein the Pdefeat for the combined effects is given by Pdefeat=PE1+(1−PE1)*PE2|E1+(1−PE1)*(1−PE2)*PE3|E1, E2 . . . where PE1 is the probability of defeat of effect 1 against a stage of the kill chain, PE1|E1 is the conditional probability of defeat of effect 2 given effect 1 against a different stage of the kill chain.

10. The method of claim 1 , wherein the Pdefeat for the combined effects is given by Pdefeat=Pdefeat,cyber+(1−Pdefeat,cyber)*Pdefeat,EW+(1−Pdefeatcyber)*(1−Pdefeat,EW)*Pdefeat kinetic where Pdefeat, cyber is the cumulative provability of defeat for all cyber effects, Pdefeat, EW is the cumulative probability of defeat for all electronic warfare effects and Pdefeat,kinetic is the cumulative probability of defeat for all kinetic effects against different stages of the kill chain.

11. The method of claim 1 , wherein Pnegate=Psv|Psu)*(Psu|Pa)*(Pa|Ppl)*Ppl is the probability of defeating a threat for a cyber effect and where Ppl is the probability of placement of the cyber effect, Pa is the probability of activation of the cyber effect, Psu is the probability of success of the cyber effect and Psv is a probability indicating the extent of damage to the target that constitute the set of hierarchical parameters ingestible by the SMM.

12. The method of claim 1 , further comprising injecting variability in the input and results data organically through variations in sensor inputs or simulated data.

13. The method of claim 1 , further comprising injecting variability in the input and results data synthetically by introducing random numbers according to a probability distribution function.

14. The method of claim 4 , further comprising feedback Battle Damage Assessment (BDA) data to the SMM to update effect/threat vulnerability, probabilities and constraints.

15. The method of claim 1 , further comprising displaying the probabilities and confidence intervals for each hierarchical parameter for one or more effects.

16. The method of claim 12 , further comprising identifying a parameter in the set of hierarchical parameters having the lowest probability or widest confidence interval as a weakest parameter.

17. The method of claim 1 , further comprising generating scenarios at multiple security levels depending on the security classification of the input and results data.

18. A method of analyzing kinetic and non-kinetic effects for negating kinetic and non-kinetic threats, the method comprising:
defining a threat kill chain for one or more kinetic or non-kinetic threats;
generating a scenario that combines kinetic and non-kinetic effects to engage the one or more kinetic or non-kinetic threats at different stages of the threat kill chain;
applying effect-specific analysis tools to one or more of the kinetic and non-kinetic effects in the scenario to individually negate one of the one or more threats and generate results data for each said effect-threat pair;
normalizing the results data for each said effect to a set of hierarchical parameters ingestible by a stochastic math model (SMM);
iteratively running the SMM on the results data to compute a probability of defeat (Pdefeat) with an associated confidence interval (CI) for each said effect and the combined effects at each stage of the threat kill chain; and
displaying the Pdefeat and CIs for each said effect and the combined effects at each stage of the threat kill chain.

19. The method of claim 18 , wherein the steps of normalizing the results data comprises applying a rules-based module to map the results data to probabilities values for each parameter in the set of hierarchical parameters in a delimited file format ingestible by the SMM.

20. The method of claim 18 , wherein a discrete event simulator (DES) is configured to implement the SMM and perform a Monte Carlo simulation on organically or synthetically varied input and results data to generate the Pdefeat and confidence intervals.

Assignees

• Raytheon Co

Inventors

• Hershey Paul Christian
• Zett Marilyn Winklareth
• Cianciosi Ii Michael Angelo
• Hoppes Brianne Rene-Martinek
• Chang Roland Dige
• Arnold Andrew
• Zolper Jr John

Classifications

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• F41H11/02 Primary

  Anti-aircraft or anti-guided missile {or anti-torpedo} defence installations or systems (cartridges or missiles for producing smoke or for dispensing radar chaff or infrared material F42B5/15, F42B12/48, F42B12/70; {informative reference: details of radar or sonar systems G01S7/00; jamming or electronic countermeasures G01S7/38, G01S7/495, H04K3/00}) · CPC title

• F41H11/00

  Defence installations; Defence devices (constructional aspects see Section E, e.g. {air-raid shelters} E04H9/04; {protective arrangements for buildings E04B1/92; extinguishing or preventing the spread of fire from, incendiary bombs A62C3/06; dynamic armour F41H5/007; ballistically deployed systems for restraining persons or animals F41H13/0006; electronic countermeasures G01S}) · CPC title

• G06N7/005

  Physics · mapped topic

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title