Abstract:
A method of rapidly producing a new cyber response tool (e.g., in near-real-time) by matching vulnerabilities of enemy threats (e.g., a missile and/or a tank) to corresponding portions of other response tools that effectively exploit the matched vulnerability. An iterative framework may be utilized to repeatedly prioritize a set of cyber response tools based on a corresponding probability of success. For example, a computer or computer network may implement the iterative framework to carry out the probability computation and corresponding cyber response tool prioritization. If a total probability of success is below a given threshold (e.g., 95%), then creation of one or more new cyber response tools may be initiated. The probability of success may be a function of time (e.g., ten minutes before an expected launch) and/or a function of a phase of a lifecycle of the enemy threat (e.g., a launch phase).
Abstract:
In one aspect, a method includes receiving, at a first node in a network, a resource reservation request from a second node in the network, determining, at the first node, if there is another node in the network that can be used to reach a destination and meet the resource reservation request and notifying the second node a result of the determining.
Abstract:
Systems, devices, methods, and computer-readable media for course of action (COA) analysis and execution. A method can include receiving COA data, coordinating, by an orchestrator service, simulation of performing COAs associated with the COA data, generating a graphical view of the simulation of the COAs including scores associated with each COA, implementing a COA of the COAs selected by the commander, receiving, from multiple applications information regarding a state of executing the COA, and providing a graphical view of the state of executing the COA including an overall map of a geographical region in which the COA is implemented, the graphical view including a dynamic location of the threat and threat mitigation activities, and a dynamic view of the LOS updated as the COA is implemented.
Abstract:
Discussed generally are techniques for managing operation of programs in a sequential order. A method can include receiving a query for an image, the query indicating characteristics of the image, selecting a chain of algorithms configured to identify the image based on the characteristics, operating an algorithm of the selected chain of algorithms that operate in increased fidelity order on an input to produce a first result, operating a ground truth algorithm on the input to generate a second result, comparing the first and second results to determine a probability of correctness (Pc) and confidence interval (CI) for the algorithm, and altering the chain of algorithms based on the determined Pc and CI.
Abstract:
A computing machine stores representations of a plurality of assets. The computing machine receives a representation of a course of action (COA), the COA making use of all or a subset of the plurality of assets, a representation of movement of the all or the subset of the plurality of assets across time, and a goal. The computing machine identifies one or more mission phases and/or activities in the COA and one or more assets or asset pairings for use in each mission phase and/or activity. The computing machine computes a set of values for a given mission phase and/or activity. The computing machine logs the computed set of values. The computing machine stores metrics representing the computed set of values and the one or more mission phases and/or activities in the COA. The metrics are used to verify the results of the computations.
Abstract:
A system and method improves the probability of correctly detecting an object from a collection of source data and reduces the processing load. A plurality of algorithms for a given data type are selected and ordered based on a cumulative trained probability of correctness (Pc) that each of the algorithms, which are processed in a chain and conditioned upon the result of the preceding algorithms, produce a correct result and a processing. The algorithms cull the source data to pass forward a reduced subset of source data in which the conditional probability of detecting the object is higher than the a priori probability of the algorithm detecting that same object. The Pc and its confidence interval is suitably computed and displayed for each algorithm and the chain and the final object detection.
Abstract:
A system and method improves the probability of correctly detecting an object from a collection of source data and reduces the processing load. A plurality of algorithms for a given data type are selected and ordered based on a cumulative trained probability of correctness (Pc) that each of the algorithms, which are processed in a chain and conditioned upon the result of the preceding algorithms, produce a correct result and a processing. The algorithms cull the source data to pass forward a reduced subset of source data in which the conditional probability of detecting the object is higher than the a priori probability of the algorithm detecting that same object. The Pc and its confidence interval is suitably computed and displayed for each algorithm and the chain and the final object detection.
Abstract:
A method of rapidly producing a new cyber response tool (e.g., in near-real-time) by matching vulnerabilities of enemy threats (e.g., a missile and/or a tank) to corresponding portions of other response tools that effectively exploit the matched vulnerability. An iterative framework may be utilized to repeatedly prioritize a set of cyber response tools based on a corresponding probability of success. For example, a computer or computer network may implement the iterative framework to carry out the probability computation and corresponding cyber response tool prioritization. If a total probability of success is below a given threshold (e.g., 95%), then creation of one or more new cyber response tools may be initiated. The probability of success may be a function of time (e.g., ten minutes before an expected launch) and/or a function of a phase of a lifecycle of the enemy threat (e.g., a launch phase).
Abstract:
This document discusses, among other things, apparatus and methods for context-aware mission management. In an example, a system can include a processing center and a plurality of mobile devices. The processing center can be configured to receive one or more mission objectives for one or more mission stages, and to receive information from a plurality of sources, the processing center including a data broker reasoner (DBR) configured to compare the information to the one or more mission objectives and to provide role-based task information for accomplishing the one or more mission objectives. The plurality of mobile devices can be configured to wirelessly communicate with the processing center, to receive login information, to register with the processing center using the login information and to display a portion of the role-based task information associated with the login information.
Abstract:
Techniques for machine learning-assisted multi-domain planning are disclosed, including: receiving first situational data from a first domain and second situational data from a second domain; receiving first user input indicating an objective; applying at least the first situational data, the second situational data, and the objective to a machine learning model, to obtain one or more suggested courses of action for satisfying the objective using assets selected from a plurality of assets available in the first domain and the second domain; and presenting the one or more suggested courses of action in a graphical user interface.