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US 11,388,044 B1Granted
Independent situational awareness for a network
AssigneePeraton Labs Inc.
InventorsChi Leung Lau, David Shur, Qiong Shen, Kiran Rege, Rajesh Krishnan, Ta Chen, Andrzej Cichocki, Tom Banwell
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Abstract
A computer-program product, a system, and a computer-implemented method include a processor(s) obtaining a configuration of a network including configurations of multiple network nodes and configurations of the network communication devices. The program code automatically models the network to generate a system model. The program code derives, from the system model, a loop-free Bayesian inference model, by generating a loop-free Bayesian network from the network.
Claims16
A system comprising:
- a network comprising multiple nodes, wherein the network is a mesh network comprising multiple intertwining loops, wherein the network comprises a supervisory control and data acquisition (SCADA) system;
- one or more sensors deployed on a pre-determined portion of the multiple nodes, wherein the pre-determined portion comprises a number of nodes comprising a specific fraction of the multiple nodes, wherein the one or more sensors is a minimum number of sensors in common grid network topologies able to provide situational awareness when the supervisory control and data acquisition (SCADA) system is inaccessible by the one or more processors;
- one or more communication devices communicatively coupled to the one or more sensors to obtain measurements from the one or more sensors;
- a memory;
- one or more processors communicatively coupled to the one or more sensors and in communication with the memory;
- and
- program instructions executable by the one or more processors, via the memory, to perform a method, the method comprising:
- obtaining, by the one or more processors, a configuration of the network, wherein the configuration of the network comprises configurations of the multiple nodes and configurations of the communication devices;
- based on obtaining the configuration, automatically modeling, by the one or more processors, the network to generate a system model;
- deriving, by the one or more processors, from the system model, a loop-free Bayesian inference model, wherein deriving the loop-free Bayesian inference model comprises generating a loop-free Bayesian network from the network, wherein the loop-free Bayesian network has a one-to-one correspondence with physical elements of the network;
- obtaining, by the one or more processors, an indication of an event in the network;
- determining, by the one or more processors, that the supervisory control and data acquisition (SCADA) system is inaccessible by the one or more processors
- based on obtaining the indication and determining that the supervisory control and data acquisition (SCADA) system is inaccessible, obtaining, by the one or more processors, the measurements, from the one or more communications devices;
- applying, by the one or more processors, the loop-free Bayesian inference model, to the measurements to obtain situational awareness for the specific fraction of the multiple nodes and to derive, based on inference, situational awareness for a remainder of the multiple nodes;
- and
- utilizing, by the one or more processors, the situational awareness for the specific fraction of the multiple nodes, the situational awareness for the remainder of the multiple nodes, and the one-to-one correspondence, to locate the event.
13 more elements
A computer-implemented method comprising:
- obtaining, by one or more processors, a configuration of a network, wherein the network is a mesh network comprising multiple intertwining loops and a supervisory control and data acquisition (SCADA) system, and wherein the network further comprises:
- multiple nodes;
- one or more sensors on a pre-determined portion of the multiple nodes, wherein the pre-determined portion comprises a number of nodes comprising a specific fraction of the multiple nodes, wherein the one or more sensors is a minimum number of sensors in common grid network topologies able to provide situational awareness when the supervisory control and data acquisition (SCADA) system is inaccessible by the one or more processors;
- and
- one or more communication devices communicatively coupled to the one or more sensors to obtain measurements from the one or more sensors, wherein the configuration of the network comprises configurations of the multiple nodes and configurations of the communication devices;
- based on obtaining the configuration, automatically modeling, by the one or more processors, the network to generate a system model;
- and
- deriving, by the one or more processors, from the system model, a loop-free Bayesian inference model, wherein deriving the loop-free Bayesian inference model comprises generating a loop-free Bayesian network from the network;
- obtaining, by the one or more processors, an indication of an event in the network;
- determining, by the one or more processors, that the supervisory control and data acquisition (SCADA) system is inaccessible by the one or more processors;
- based on obtaining the indication and determining that the supervisory control and data acquisition (SCADA) system is inaccessible, obtaining, by the one or more processors, the measurements, from the one or more communications devices;
- applying, by the one or more processors, the loop-free Bayesian inference model, to the measurements to obtain situational awareness for the specific fraction of the multiple nodes and to derive, based on inference, situational awareness for a remainder of the multiple nodes;
- and
- utilizing, by the one or more processors, the situational awareness for the specific fraction of the multiple nodes, the situational awareness for the remainder of the multiple nodes, and the one-to-one correspondence, to locate the event.
11 more elements
A computer program product comprising:
- a computer readable storage medium readable by one or more processors and storing instructions for execution by the one or more processors for performing a method comprising:
- obtaining, by the one or more processors, a configuration of a network, wherein the network is a mesh network comprising multiple intertwining loops, and wherein the network comprises:
- multiple nodes;
- one or more sensors on a pre-determined portion of the multiple nodes, wherein the pre-determined portion comprises a number of nodes comprising a specific fraction of the multiple nodes;
- and
- one or more communication devices communicatively coupled to the one or more sensors to obtain measurements from the one or more sensors, wherein the configuration of the network comprises configurations of the multiple nodes and configurations of the communication devices;
- based on obtaining the configuration, automatically modeling, by the one or more processors, the network to generate a system model;
- and
- deriving, by the one or more processors, from the system model, a loop-free Bayesian inference model, wherein deriving the loop-free Bayesian inference model comprises generating a loop-free Bayesian network from the network;
- obtaining, by the one or more processors, an indication of an event in the network;
- determining, by the one or more processors, that the supervisory control and data acquisition (SCADA) system is inaccessible by the one or more processors;
- based on obtaining the indication and determining that the supervisory control and data acquisition (SCADA) system is inaccessible, obtaining, by the one or more processors, the measurements, from the one or more communications devices;
- applying, by the one or more processors, the loop-free Bayesian inference model, to the measurements to obtain situational awareness for the specific fraction of the multiple nodes and to derive, based on inference, situational awareness for a remainder of the multiple nodes;
- and
- utilizing, by the one or more processors, the situational awareness for the specific fraction of the multiple nodes, the situational awareness for the remainder of the multiple nodes, and the one-to-one correspondence, to locate the event.
12 more elements
Dependencies are inferred from the claim text.