Network monitoring method using phantom nodes

• US 9,960,956 B1
• Filed: 09/14/2015
• Issued: 05/01/2018
• Est. Priority Date: 10/15/2014
• Status: Active Grant

• Current Assignee: The Government Of The United States Of America, As Represented By The Secretary Of The Navy
• Original Assignee: the united states of america as represented by the secretary of the navy
• Inventors: Johnson, Jamie Lynn, Parker, Thomas, Tummala, Murali, McEachen, John C.
• Primary Examiner(s): Kim, Edward
• Application Number:
  US14/852,734
• Time in Patent Office: 960 Days
• Field of Search: 709224
• CPC Class Codes: H04L 41/12 Discovery or management of network topologies
• CPC Class Codes: H04L 41/122 of virtualised topologies, e.g. software-defined networks [SDN] or network function virtualisation [NFV]
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/40 using virtualisation of network functions or resources, e.g. SDN or NFV entities
• CPC Class Codes: H04L 43/0888 Throughput
• CPC Class Codes: H04L 43/20 the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV
• CPC Class Codes: H04L 45/28 using route fault recovery
• CPC Class Codes: H04L 47/11 Identifying congestion
• CPC Class Codes: H04L 47/125 by balancing the load, e.g. traffic engineering
• CPC Class Codes: H04L 49/557 Error correction, e.g. fault recovery or fault tolerance

Abstract

Determining flow rules in a software defined network (SDN) of a plurality of forwarding devices includes determining, by a controller device, a network adjacency matrix of the SDN, wherein the network adjacency matrix represents a topology of the SDN; placing, by the controller device, a phantom node in the network adjacency matrix, wherein the phantom node does not physically exist within the topology of the SDN and the phantom node is attached to a first node with maximum degree in the network adjacency matrix to create a phantom adjacency matrix, wherein the first node corresponds to a first forwarding device in the SDN; and determining, by the controller device, an adverse condition in the SDN using the phantom node, wherein the controller device is separate from the plurality of forwarding devices.

Claims

1. A system for determining node congestion in a dynamic network comprising:

a nodal network, where the nodal network comprises a quantity of n nodes, where every node comprising the quantity of n nodes comprises a forwarding device, and where the every node has a communications link to at least one other node comprising the quantity of n nodes, where the communications link has a maximum bandwidth X_MAXIMUM;

a programmable controller in data communication with each node comprising the quantity of n nodes, where the programmable controller is programmed to perform steps comprising;

polling one or more nodes comprising the quantity of n nodes and determining a throughput X_MEASURED for each communications link comprising the nodal network;

defining a link weight for the each communications link using the X_MAXIMUM for the each communications link and using the X_MEASURED for the each communications link;

creating one or more phantom nodes, where each phantom node does not physically exist within the nodal network, and where the one or more phantom nodes comprises a quantity of m phantom nodes, and where each phantom node is a representation of an additional node in the nodal network, by;

devising a phantom link between the each phantom node and one of the nodes comprising the quantity of n nodes, thereby devising one or more phantom links; and

assigning a phantom link weight for each phantom link in the one or more phantom links, thereby assigning one or more phantom link weights and creating the one or more phantom nodes;

developing a plurality of analysis nodes where the plurality of analysis nodes comprises the quantity of n nodes and the quantity of m phantom nodes and designating each separate node and each separate phantom node in the plurality of analysis nodes with a node designation, where the each separate node and the each separate phantom node has a unique node designation;

generating a Graph representing the plurality of analysis nodes, where the Graph comprises a representation for each individual node comprising the quantity of n nodes, each individual phantom node comprising the quantity of m phantom nodes, every link weight for every communications link in the nodal network, and every phantom link weight; and

analyzing the Graph by performing steps comprising;

determining a Laplacian matrix Q describing the Graph and using the node designations;

finding an eigenvector matrix V using the Laplacian matrix Q, where the eigenvector matrix V is an (n+m) by (n+m) matrix comprising elements ν

_i^l, where i is an eigenindex and where l is one of the node designations; and

determining node congestion by, identifying at least two nodal influencers by selecting at least a first eigenindex i(1) and a second eigenindex i(2), where i(1) is less than i(2), and performing steps comprising;

finding a primary influencer of the first eigenindex i(1) by inspecting the eigenvector matrix V and finding for the first eigenindex i(1) a maximum ν

_i(1)^lmax(1), where the maximum ν

_i(1)^lmax(1) is an element ν

_i^l of the eigenvector matrix V having a maximum magnitude among all elements ν

_i^l of the eigenvector matrix V when the eigenindex i is equal to the first eigenindex i(1), and designating the node corresponding to the node designation lmax(1) of the maximum ν

_i(1)^lmax(1) as the primary influencer of the first eigenindex i(1);

ascertaining a primary influencer of the second eigenindex i(2) by inspecting the eigenvector matrix V and finding for the second eigenindex i(2) a maximum ν

_i(2)^lmax(2), where the maximum ν

_i(2)^lmax(2) is an element ν

_i^l of the eigenvector matrix V having a maximum magnitude among all elements ν

_i^l of the eigenvector matrix V when the eigen index i is equal to the second eigenindex i(2), and designating the node corresponding to the node designation lmax(2) of the maximum ν

_i(2)^lmax(2) as the primary influencer of the second eigenindex i(2); and

identifying, if the primary influencer of the first eigenindex i(1) is one of the quantity of n nodes comprising the nodal network and the primary influencer of the second eigenindex i(2) is one of the one or more phantom nodes, the primary influencer of the first eigenindex i(1) as a congested node, thereby determining node congestion in the dynamic network.

2. The system of claim 1 where the programmable controller is programmed to determine the Laplacian matrix Q describing the Graph and using the node designations by performing steps comprising:

determining a phantom adjacency matrix A using the Graph and using the node designations;

determining a degree matrix D using the Graph and using the node designations; and

determining the Laplacian matrix Q by subtracting the degree matrix D from the phantom adjacency matrix A.

3. The system of claim 2 where the programmable controller is programmed to determine the phantom adjacency matrix A using the Graph and using the node designations by performing steps comprising:

establishing a plurality of link weights w_ij where each link weight w_ij comprises either a link weight for the communication links comprising the nodal network or a phantom link weight phantom link weight for one of the phantom links in the one or more phantom links, and where each link weight w_ij represents a link between an i^th node and a j^th node, where the i^th node corresponds to a first node designation and the j^th node corresponds to a second node designation; and

determining the phantom adjacency matrix A using the plurality of link weights w_ij, where the phantom adjacency matrix A comprises elements A(i,j) where the elements A(i,j) comprise the plurality of link weights w_ij.

4. The system of claim 2 where the programmable controller is programmed to determine the phantom adjacency matrix A using the plurality of link weights w_ij by performing steps comprising:

establishing the phantom adjacency matrix A as an (n+m) by (n+m) matrix comprising elements A(i, j) where an element A(i, j) corresponds to the link between the i^th node and the j^th node; and

defining each element A(i, j) where the each element A(i, j) comprises the link weight w_ij corresponding to the link between the i^th node and the j^th node.

5. The system of claim 4 where the programmable controller is programmed to determine the degree matrix D by performing steps comprising:

establishing the degree matrix D as another (n+m) by (n+m) matrix comprising elements D(i, j);

determining a w_ij summation for each specific i^th node comprising the plurality of analysis nodes, where the w_ij summation comprises a summation of the link weights w_ij representing the link between the i^th node and the j^th node when the each specific i^th node is the i^th node; and

defining each element D(i, j) where the each element D(i, j) comprises the w_ij summation for an individual i^th node comprising the plurality of analysis nodes when both i and j are equal to the node designation of the individual i^th node, and where the each element D(i, j) is equal to zero when both i and j are not equal to the node designation of the individual i^th node.

6. The system of claim 1 where the every node comprising the quantity of n nodes in the nodal network comprises a routing table, and where the programmable controller is further programmed to perform steps comprising communicating a flow level routing instruction to at least one node comprising the nodal network based on the node congestion.

7. The system of claim 6 where the programmable controller is further programmed to identify an attacked node by performing steps comprising:

repeating, after the programmable controller communicates the flow level routing instruction to the at least one node comprising the nodal network, the polling one or more nodes step, the defining the link weight for the each communications link step, the generating the Graph representing the plurality of analysis nodes step, the analyzing the Graph step, the determining the Laplacian matrix Q step, and the finding the eigenvector matrix V step, the determining node congestion step, the finding the primary influencer of the first eigenindex i(1) step, and the ascertaining the primary influencer of the second eigenindex i(2) step; and

identifying, if the primary influencer of the first eigenindex i(1) is the one of the quantity of n nodes comprising the nodal network and the primary influencer of the second eigenindex i(2) is the one of the one or more phantom nodes, the primary influencer of the first eigenindex i(1) as an attacked node.

8. The system of claim 6 where the programmable controller is further programmed to operate in a cyclic manner by performing steps comprising:

creating one or more new phantom nodes, where the one or more new phantom nodes comprises a quantity of m′

new phantom nodes, and where each new phantom node is a representation of a new additional node in the nodal network, by;

devising a new phantom link between the each new phantom node and one of the nodes comprising the quantity of n nodes, thereby devising one or more new phantom links; and

assigning a new phantom link weight for each new phantom link in the one or more new phantom links, thereby assigning one or more new phantom link weights and creating the one or more new phantom nodes; and

repeating the polling one or more nodes step, the defining the link weight for the each communications link step, the developing the plurality of analysis nodes step, the generating the Graph representing the plurality of analysis nodes step, the analyzing the Graph step, the determining the Laplacian matrix Q step, the finding the eigenvector matrix V step, the determining node congestion step, the finding the primary influencer of the first eigenindex i(1) step, the ascertaining the primary influencer of the second eigenindex i(2) step, and the identifying, if the primary influencer of the first eigenindex i(1) is one of the quantity of n nodes comprising the nodal network and the primary influencer of the second eigenindex i(2) is one of the one or more phantom nodes, the primary influencer of the first eigenindex i(1) as the congested node step, by using the one or more new phantom nodes as the one or more phantom nodes, and using the quantity of m′

new phantom nodes as the quantity of m phantom nodes, and using the one or more new phantom links as the one or more phantom links, and using the one or more new phantom link weights as the one or more phantom link weights.

9. A non-transitory program storage device readable by a programmable controller and tangibly embodying a program of instructions executable by the programmable controller to perform a method for determining node congestion in a software defined network (SDN) of a plurality of forwarding devices, the program of instructions directing the programmable controller to perform steps comprising:

polling one or more nodes comprising a nodal network, where the nodal network comprises a quantity of n nodes, where every node comprising the quantity of n nodes comprises a forwarding device, and where the every node has a communications link to at least one other node comprising the quantity of n nodes, where the communications link has a maximum bandwidth X_MAXIMUM, and defining a link weight for each communications link in the nodal network by;

determining a throughput X_MEASURED for the each communications link comprising the nodal network; and

defining a link weight for the each communications link using the X_MAXIMUM for the each communications link and using the X_MEASURED for the each communications link;

creating one or more phantom nodes, where each phantom node does not physically exist within the nodal network, and where the one or more phantom nodes comprises a quantity of m phantom nodes, and where each phantom node is a representation of an additional node in the nodal network, by;

devising a phantom link between the each phantom node and one of the nodes comprising the quantity of n nodes, thereby devising one or more phantom links; and

assigning a phantom link weight for each phantom link in the one or more phantom links, thereby assigning one or more phantom link weights and creating the one or more phantom nodes;

developing a plurality of analysis nodes where the plurality of analysis nodes comprises the quantity of n nodes and the quantity of m phantom nodes and designating each separate node and each separate phantom node in the plurality of analysis nodes with a node designation, where the each separate node and the each separate phantom node has a unique node designation;

generating a Graph representing the plurality of analysis nodes, where the Graph comprises a representation for each individual node comprising the quantity of n nodes, each individual phantom node comprising the quantity of m phantom nodes, every link weight for every communications link in the nodal network, and every phantom link weight; and

analyzing the Graph by performing steps comprising;

determining a Laplacian matrix Q describing the Graph and using the node designations;

finding an eigenvector matrix V using the Laplacian matrix Q, where the eigenvector matrix V is an (n+m) by (n+m) matrix comprising elements ν

_i^l, where i is an eigenindex and where l is one of the node designations; and

determining, node congestion by identifying at least two nodal influencers by selecting at least a first eigenindex i(1) and a second eigenindex i(2), where i(1) is less than i(2), and performing steps comprising;

finding a primary influencer of the first eigenindex i(1) by inspecting the eigenvector matrix V and finding for the first eigenindex i(1) a maximum ν

_i(1)^lmax(1), where the maximum ν

_i(1)^lmax(1) is an element ν

_i^l of the eigenvector matrix V having a maximum magnitude among all elements ν

_i^l of the eigenvector matrix V when the eigen index i is equal to the first eigenindex i(1), and designating the node corresponding to the node designation lmax(1) of the maximum ν

_i(1)^lmax(1) as the primary influencer of the firsteigenindex i(1);

ascertaining a primary influencer of the second eigenindex i(2) by inspecting the eigenvector matrix V and finding for the second eigenindex i(2) a maximum ν

_i(2)^lmax(2), where the maximum ν

_i(2)^lmax(2) is an element ν

_i^l of the eigenvector matrix V having a maximum magnitude among all elements ν

_i^l of the eigenvector matrix V when the eigen index i is equal to the second eigenindex i(2), and designating the node corresponding to the node designation lmax(2) of the maximum ν

_i(2)^lmax(2) as the primary influencer of the second eigenindex i(2); and

identifying, if the primary influencer of the first eigenindex i(1) is one of the quantity of n nodes comprising the nodal network and the primary influencer of the second eigenindex i(2) is one of the one or more phantom nodes, the primary influencer of the first eigenindex i(1) as a congested node, thereby determining node congestion.

10. The non-transitory program storage device of claim 9 where the program of instructions further directs the programmable controller to determine the Laplacian matrix Q describing the Graph and using the node designations by performing steps comprising:

determining a phantom adjacency matrix A using the Graph and using the node designations;

determining a degree matrix D using the Graph and using the node designations; and

determining the Laplacian matrix Q by subtracting the degree matrix D from the phantom adjacency matrix A.

11. The non-transitory program storage device of claim 10 where the program of instructions further directs the programmable controller to determine the phantom adjacency matrix A using the Graph and using the node designations by performing steps comprising:

establishing a plurality of link weights w_ij where each link weight w_ij comprises either a link weight for the communication links comprising the nodal network or a phantom link weight phantom link weight for one of the phantom links in the one or more phantom links, and where each link weight w_ij represents a link between an i^th node and a j^th node, where the i^th node corresponds to a first node designation and the j^th node corresponds to a second node designation; and

determining the phantom adjacency matrix A using the plurality of link weights w_ij, where the phantom adjacency matrix A comprises elements A(i,j) where the elements A(i,j) comprise the plurality of link weights w_ij.

12. The non-transitory program storage device of claim 11 where the program of instructions further directs the programmable controller to determine the phantom adjacency matrix A using the plurality of link weights w_ij by performing steps comprising:

establishing the phantom adjacency matrix A as an (n+m) by (n+m) matrix comprising elements A(i, j) where an element A(i, j) corresponds to the link between the i^th node and the j^th node; and

defining each element A(i, j) where the each element A(i, j) comprises the link weight w_ij corresponding to the link between the i^th node and the j^th node.

13. The non-transitory program storage device of claim 12 where the program of instructions further directs the programmable controller to determine the degree matrix D by performing steps comprising:

establishing the degree matrix D as another (n+m) by (n+m) matrix comprising elements D(i, j);

determining a w_ij summation for each specific i^th node comprising the plurality of analysis nodes, where the w_ij summation comprises a summation of the link weights w_ij representing the link between the i^th node and the j^th node when the each specific i^th node is the i^th node; and

defining each element D(i, j) where the each element D(i, j) comprises the w_ij summation for an individual i^th node comprising the plurality of analysis nodes when both i and j are equal to the node designation of the individual i^th node, and where the each element D(i, j) is equal to zero when both i and j are not equal to the node designation of the individual i^th node.

14. The non-transitory program storage device of claim 9 where the program of instructions further directs the programmable controller to perform steps comprising communicating a flow level routing instruction to at least one node comprising the nodal network based on the node congestion.

15. The non-transitory program storage device of claim 14 where the program of instructions further directs the programmable controller to identify an attacked node by performing steps comprising:

repeating, after communicating the flow level routing instruction to the at least one node comprising the nodal network, the polling one or more nodes step, the defining the link weight for the each communications link step, the generating the Graph representing the plurality of analysis nodes step, the analyzing the Graph step, the determining the Laplacian matrix Q step, and the finding the eigenvector matrix V step, the determining node congestion step, the finding the primary influencer of the first eigenindex i(1) step, and the ascertaining the primary influencer of the second eigenindex i(2) step; and

identifying, if the primary influencer of the first eigenindex i(1) is the one of the quantity of n nodes comprising the nodal network and the primary influencer of the second eigenindex i(2) is the one of the one or more phantom nodes, the primary influencer of the first eigenindex i(1) as an attacked node.

16. The non-transitory program storage device of claim 14 where the program of instructions further directs the programmable controller to operate in a cyclic manner by performing steps comprising:

creating one or more new phantom nodes, where the one or more new phantom nodes comprises a quantity of m′

new phantom nodes, and where each new phantom node is a representation of a new additional node in the nodal network, by;

devising a new phantom link between the each new phantom node and one of the nodes comprising the quantity of n nodes, thereby devising one or more new phantom links; and

assigning a new phantom link weight for each new phantom link in the one or more new phantom links, thereby assigning one or more new phantom link weights and creating the one or more new phantom nodes; and

repeating the polling one or more nodes step, the defining the link weight for the each communications link step, the developing the plurality of analysis nodes step, the generating the Graph representing the plurality of analysis nodes step, the analyzing the Graph step, the determining the Laplacian matrix Q step, the finding the eigenvector matrix V step, the determining node congestion step, the finding the primary influencer of the first eigenindex i(1) step, the ascertaining the primary influencer of the second eigenindex i(2) step, and the identifying, if the primary influencer of the first eigenindex i(1) is one of the quantity of n nodes comprising the nodal network and the primary influencer of the second eigenindex i(2) is one of the one or more phantom nodes, the primary influencer of the first eigenindex i(1) as the congested node step, by using the one or more new phantom nodes as the one or more phantom nodes, and using the quantity of m′

new phantom nodes as the quantity of m phantom nodes, and using the one or more new phantom links as the one or more phantom links, and using the one or more new phantom link weights as the one or more phantom link weights.

17. A system for determining flow rules in a communications network comprising:

a nodal network comprising a quantity of n nodes, where every node comprising the quantity of n nodes comprises a forwarding device, and where the every node comprising the quantity of n nodes in the nodal network comprises a routing table, and where the every node has a communications link to at least one other node comprising the quantity of n nodes, where the communications link has a maximum bandwidth X_MAXIMUM;

a programmable controller in data communication with each node comprising the quantity of n nodes, where the programmable controller is programmed to perform steps comprising;

I) polling one or more nodes comprising the quantity of n nodes and determining a throughput X_MEASURED for each communications link comprising the nodal network;

II) defining a link weight for the each communications link using the X_MAXIMUM for the each communications link and using the X_MEASURED for the each communications link;

III) creating one or more phantom nodes, where the one or more phantom nodes comprises a quantity of m phantom nodes, and where each phantom node is a representation of an additional node in the nodal network, by;

III)A) devising a phantom link between the each phantom node and one of the nodes comprising the quantity of n nodes, thereby devising one or more phantom links; and

III)B) assigning a phantom link weight for each phantom link in the one or more phantom links, thereby assigning one or more phantom link weights and creating the one or more phantom nodes;

IV) developing a plurality of analysis nodes where the plurality of analysis nodes comprises the quantity of n nodes and the quantity of m phantom nodes and designating each separate node and each separate phantom node in the plurality of analysis nodes with a node designation, where the each separate node and the each separate phantom node has a unique node designation;

V) generating a Graph representing the plurality of analysis nodes, where the Graph comprises a representation for each individual node comprising the quantity of n nodes, each individual phantom node comprising the quantity of m phantom nodes, every link weight for every communications link in the nodal network, and every phantom link weight; and

VI) analyzing the Graph by performing steps comprising;

VI)A) determining a phantom adjacency matrix A using the Graph and using the node designations;

VI)B) determining a degree matrix D using the Graph and using the node designations; and

VI)C) determining a Laplacian matrix Q by subtracting the degree matrix D from the phantom adjacency matrix A;

VI)D) finding an eigenvector matrix V using the Laplacian matrix Q, where the eigenvector matrix V is an (n+m) by (n+m) matrix comprising elements ν

_i^l, where i is an eigenindex and where l is one of the node designations; and

VI)E) determining node congestion by identifying at least two nodal influencers by selecting at least a first eigenindex i(1) and a second eigenindex i(2), where i(1) is less than i(2), and performing steps comprising;

VI)E)1) finding a primary influencer of the first eigenindex i(1) by inspecting the eigenvector matrix V and finding for the first eigenindex i(1) a maximum ν

_i(1)^lmax(1), where the maximum ν

_i(1)^lmax(1) is an element ν

_i^l of the eigenvector matrix V having a maximum magnitude among all elements ν

_i^l of the eigenvector matrix V when the eigen index i is equal to the first eigenindex i(1), and designating the node corresponding to the node designation lmax(1) of ν

_i(1)^lmax(1) as the primary influencer of the first eigenindex i(1);

VI)E)2) ascertaining a primary influencer of the second eigenindex i(2) by inspecting the eigenvector matrix V and finding for the second eigenindex i(2) a maximum ν

_i(2)^lmax(2), where the maximum ν

_i(2)^lmax(2) is an element ν

_i^l of the eigenvector matrix V having a maximum magnitude among all elements ν

_i^l of the eigenvector matrix V when the eigen index i is equal to the second eigenindex i(2), and designating the node corresponding to the node designation lmax(2) of ν

_i(2)^lmax(2) as the primary influencer of the second eigenindex i(2); and

VI)E)3) identifying, if the primary influencer of the first eigenindex i(1) is one of the quantity of n nodes comprising the nodal network and the primary influencer of the second eigenindex i(2) is one of the one or more phantom nodes, the primary influencer of the first eigenindex i(1) as a congested node, thereby determining node congestion;

VII) communicating a flow level routing instruction to at least one node comprising the nodal network based on the node congestion.

18. The system of claim 17 where the programmable controller is further programmed to operate in a cyclic manner by performing steps comprising:

creating one or more new phantom nodes, where the one or more new phantom nodes comprises a quantity of m′

new phantom nodes, and where each new phantom node is a representation of a new additional node in the nodal network, by;

devising a new phantom link between the each new phantom node and one of the nodes comprising the quantity of n nodes, thereby devising one or more new phantom links; and

assigning a new phantom link weight for each new phantom link in the one or more new phantom links, thereby assigning one or more new phantom link weights and creating the one or more new phantom nodes; and

repeating step I), step II), step IV), step V), step VI), step VI)A), step VI)B), step VI)C), step VI)D), step VI)E), step VI)E)1), step, VI)E)2), and step VI)E)3) using the one or more new phantom nodes as the one or more phantom nodes, and using the quantity of m′

new phantom nodes as the quantity of m phantom nodes, and using the one or more new phantom links as the one or more phantom links, and using the one or more new phantom link weights as the one or more phantom link weights.

19. The system of claim 18 where the programmable controller is programmed to determine the phantom adjacency matrix A using the Graph and using the node designations by performing steps comprising:

establishing a plurality of link weights w_ij where each link weight w_ij comprises either a link weight for the communication links comprising the nodal network or a phantom link weight for one of the phantom links in the one or more phantom links, and where each link weight w_ij represents a link between an i^th node and a j^th node, where the i^th node corresponds to a first node designation and the j^th node corresponds to a second node designation;

establishing the phantom adjacency matrix A as an (n+m) by (n+m) matrix comprising elements A(i, j) where an element A(i, j) corresponds to the link between the i^th node and the j^th node; and

defining each element A(i, j) where the each element A(i, j) comprises the link weight w_ij corresponding to the link between the i^th node and the j^th node.

20. The system of claim 19 where the programmable controller is programmed to determine the degree matrix D by performing steps comprising:

establishing the degree matrix D as another (n+m) by (n+m) matrix comprising elements D(i, j);

determining a w_ij summation for each specific i^th node comprising the plurality of analysis nodes, where the w_ij summation comprises a summation of the link weights w_ij representing the link between the i^th node and the j^th node when the each specific i^th node is the i^th node; and

defining each element D(i, j) where the each element D(i, j) comprises the w_ij summation for an individual i^th node comprising the plurality of analysis nodes when both i and j are equal to the node designation of the individual i^th node, and where the each element D(i, j) is equal to zero when both i and j are not equal to the node designation of the individual i^th node.

21. The system of claim 17 where the programmable controller is further programmed to identify an attacked node by performing steps comprising:

repeating, after step VII), step I), step II), step IV), step V), step VI), step VI)A), step VI)B), step VI)C), step VI)D), step VI)E), step VI)E)1), step, VI)E)2), and step VI)E)3); and

identifying, if the primary influencer of the first eigenindex i(1) is the one of the quantity of n nodes comprising the nodal network and the primary influencer of the second eigenindex i(2) is the one of the one or more phantom nodes, the primary influencer of the first eigenindex i(1) as an attacked node.