US2009328126A1PendingUtilityA1

Neural networks for ingress monitoring

52
Individually held — no corporate assignee on recordPriority: Jul 20, 1999Filed: Sep 4, 2009Published: Dec 31, 2009
Est. expiryJul 20, 2019(expired)· nominal 20-yr term from priority
Inventors:Gary W. Sinde
H04N 17/00
52
PatentIndex Score
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Cited by
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References
0
Claims

Abstract

A method of identifying a source of ingress into a network includes storing frequency spectra of known sources of ingress, comparing the frequency spectrum of ingress to the frequency spectra of known sources of ingress, and determining from the comparison which of the frequency spectra of known sources of ingress is closest to the frequency spectrum of the ingress. Apparatus for identifying a source of ingress into a network includes memory for storing frequency spectra of known sources of ingress and a device for comparing the frequency spectrum of the ingress to frequency spectra of known sources of ingress and determining from the comparison which frequency spectrum of a known source of ingress is closest to the frequency spectrum of the ingress. A method of establishing ingress into a network includes developing a first frequency spectrum indicative of the condition of the network at a first time during the operation of the network, developing a second frequency spectrum indicative of the condition of the network at a second, later time, comparing the second frequency spectrum to the first frequency spectrum, and determining from the comparison a condition of the network at the second time. Apparatus for establishing ingress into a network includes a device for receiving frequency spectra. The device receives at least one first frequency spectrum indicative of the condition of the network at a first time during the operation of the network, and a second frequency spectrum indicative of the condition of the network at a second, later time. The device compares the second frequency spectrum to the first frequency spectrum and determines from the comparison the condition of the network at the second time.

Claims

exact text as granted — not AI-modified
1 - 40 . (canceled) 
   
   
       41 . A method of establishing ingress into a network including developing a first frequency spectrum indicative of the condition of the network at a first time during the operation of the network, developing a second frequency spectrum indicative of the condition of the network at a second, later time, comparing the second frequency spectrum to the first frequency spectrum, and determining from the comparison a condition of the network at the second time. 
   
   
       42 . The method of  claim 41  wherein the first frequency spectrum indicative of the condition of the network at a first time during the operation of the network includes a first frequency spectrum indicative of a baseline condition of the network. 
   
   
       43 . The method of  claim 41  wherein comparing the second frequency spectrum to the first frequency spectrum and determining from the comparison the condition of the network at the second time together include finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum. 
   
   
       44 . The method of  claim 43  wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum includes teaching a neural network the first frequency spectrum. 
   
   
       45 . The method of  claim 44  wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum includes using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum. 
   
   
       46 . The method of  claim 45  wherein teaching a neural network the first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum. 
   
   
       47 . The method of  claim 41  further including digitizing the second frequency spectrum. 
   
   
       48 . The method of  claim 47  wherein comparing the thus-digitized second frequency spectrum to the first frequency spectrum and determining from the comparison the condition of the network at the second time together include finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum. 
   
   
       49 . The method of  claim 48  wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum includes teaching a neural network the first frequency spectrum. 
   
   
       50 . The method of  claim 49  wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum includes using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum. 
   
   
       51 . The method of  claim 50  wherein teaching a neural network the first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum. 
   
   
       52 . The method of  claim 47  wherein comparing the second frequency spectrum to the first frequency spectrum includes digitizing the first frequency spectrum. 
   
   
       53 . The method of  claim 52  wherein comparing the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum and determining from the comparison a condition of the network together include finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       54 . The method of  claim 53  wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum includes teaching a neural network the thus-digitized first frequency spectrum. 
   
   
       55 . The method of  claim 54  wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum includes using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       56 . The method of  claim 55  wherein teaching a neural network the thus-digitized first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       57 . The method of  claim 41  wherein comparing the second frequency spectrum to the first frequency spectrum includes digitizing the first frequency spectrum. 
   
   
       58 . The method of  claim 57  wherein comparing the second frequency spectrum to the thus-digitized first frequency spectrum and determining from the comparison a condition of the network together include finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       59 . The method of  claim 58  wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum includes teaching a neural network the thus-digitized first frequency spectrum. 
   
   
       60 . The method of  claim 59  wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum includes using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       61 . The method of  claim 60  wherein teaching a neural network the thus-digitized first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       62 . The method of  claim 41  wherein the at least one first frequency spectrum indicative of the condition of the network at a first time during the operation of the network includes multiple first frequency spectra indicative of the condition of the network at multiple first times during the operation of the network, and the multiple first frequency spectra are combined prior to comparing the second frequency spectrum to the combined first frequency spectra. 
   
   
       63 . The method of  claim 62  wherein the combined first frequency spectra indicative of the condition of the network at multiple first times during the operation of the network include combined first frequency spectra indicative of a baseline condition of the network. 
   
   
       64 . The method of  claim 62  wherein comparing the second frequency spectrum to the combined first frequency spectra and determining from the comparison the condition of the network at the second time together include finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra. 
   
   
       65 . The method of  claim 64  wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra includes teaching a neural network the combined first frequency spectra. 
   
   
       66 . The method of  claim 65  wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra includes using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra. 
   
   
       67 . The method of  claim 66  wherein teaching a neural network the combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra. 
   
   
       68 . The method of  claim 62  further including digitizing the second frequency spectrum. 
   
   
       69 . The method of  claim 68  wherein comparing the thus-digitized second frequency spectrum to the combined first frequency spectra and determining from the comparison the condition of the network at the second time together include finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra. 
   
   
       70 . The method of  claim 69  wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra includes teaching a neural network the combined first frequency spectra. 
   
   
       71 . The method of  claim 70  wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra includes using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra. 
   
   
       72 . The method of  claim 71  wherein teaching a neural network the combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra. 
   
   
       73 . The method of  claim 68  wherein comparing the second frequency spectrum to the combined first frequency spectra includes digitizing the first frequency spectra. 
   
   
       74 . The method of  claim 73  wherein comparing the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra and determining from the comparison a condition of the network together include finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       75 . The method of  claim 74  wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra includes teaching a neural network the thus-digitized combined first frequency spectra. 
   
   
       76 . The method of  claim 75  wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra includes using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       77 . The method of  claim 76  wherein teaching a neural network the thus-digitized combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       78 . The method of  claim 62  wherein comparing the second frequency spectrum to the combined first frequency spectra includes digitizing the combined first frequency spectra. 
   
   
       79 . The method of  claim 78  wherein comparing the second frequency spectrum to the thus-digitized combined first frequency spectra and determining from the comparison a condition of the network together include finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       80 . The method of  claim 79  wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra includes teaching a neural network the thus-digitized combined first frequency spectra. 
   
   
       81 . The method of  claim 80  wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra includes using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       82 . The method of  claim 81  wherein teaching a neural network the thus-digitized combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       83 . Apparatus for establishing ingress into a network, the apparatus including a device for receiving frequency spectra, the device receiving at least one first frequency spectrum indicative of the condition of the network at a first time during the operation of the network, the device receiving a second frequency spectrum indicative of the condition of the network at a second, later time, the device further comparing the second frequency spectrum to the first frequency spectrum and determining from the comparison the condition of the network at the second time. 
   
   
       84 . The apparatus of  claim 83  wherein the first frequency spectrum indicative of the condition of the network at a first time during the operation of the network includes a first frequency spectrum indicative of a baseline condition of the network. 
   
   
       85 . The apparatus of  claim 83  wherein the device for comparing the second frequency spectrum to the first frequency spectrum and determining from the comparison the condition of the network at the second time together include a device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum. 
   
   
       86 . The apparatus of  claim 85  wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum includes a device for teaching a neural network the first frequency spectrum. 
   
   
       87 . The apparatus of  claim 86  wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum. 
   
   
       88 . The apparatus of  claim 87  wherein the device for teaching a neural network the first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum. 
   
   
       89 . The apparatus of  claim 83  wherein the device further includes a device for digitizing the second frequency spectrum. 
   
   
       90 . The apparatus of  claim 89  wherein the device for comparing the thus-digitized second frequency spectrum to the first frequency spectrum and determining from the comparison the condition of the network at the second time together include a device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum. 
   
   
       91 . The apparatus of  claim 90  wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum includes a device for teaching a neural network the first frequency spectrum. 
   
   
       92 . The apparatus of  claim 91  wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum. 
   
   
       93 . The apparatus of  claim 92  wherein the device for teaching a neural network the first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum. 
   
   
       94 . The apparatus of  claim 89  wherein the device for comparing the second frequency spectrum to the first frequency spectrum includes digitizing the first frequency spectrum. 
   
   
       95 . The apparatus of  claim 94  wherein the device for comparing the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum and determining from the comparison a condition of the network together include a device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       96 . The apparatus of  claim 95  wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum includes a device for teaching a neural network the thus-digitized first frequency spectrum. 
   
   
       97 . The apparatus of  claim 96  wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       98 . The apparatus of  claim 97  wherein the device for teaching a neural network the thus-digitized first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       99 . The apparatus of  claim 83  wherein the device for comparing the second frequency spectrum to the first frequency spectrum includes a device for digitizing the first frequency spectrum. 
   
   
       100 . The apparatus of  claim 99  wherein the device for comparing the second frequency spectrum to the thus-digitized first frequency spectrum and determining from the comparison a condition of the network together include a device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       101 . The apparatus of  claim 100  wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum includes a device for teaching a neural network the thus-digitized first frequency spectrum. 
   
   
       102 . The apparatus of  claim 101  wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       103 . The apparatus of  claim 102  wherein the device for teaching a neural network the thus-digitized first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum. 
   
   
       104 . The apparatus of  claim 83  wherein the at least one first frequency spectrum indicative of the condition of the network at a first time during the operation of the network includes multiple first frequency spectra indicative of the condition of the network at multiple first times during the operation of the network, and the device combines the multiple first frequency spectra prior to comparing the second frequency spectrum to the combined first frequency spectra. 
   
   
       105 . The apparatus of  claim 104  wherein the combined first frequency spectra indicative of the condition of the network at multiple first times during the operation of the network include combined first frequency spectra indicative of a baseline condition of the network. 
   
   
       106 . The apparatus of  claim 104  wherein the device for comparing the second frequency spectrum to the combined first frequency spectra and determining from the comparison the condition of the network at the second time together include a device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra. 
   
   
       107 . The apparatus of  claim 106  wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra includes a device for teaching a neural network the combined first frequency spectra. 
   
   
       108 . The method of  claim 107  wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra. 
   
   
       109 . The apparatus of  claim 108  wherein the device for teaching a neural network the combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra. 
   
   
       110 . The apparatus of  claim 104  wherein the device further includes a device for digitizing the second frequency spectrum. 
   
   
       111 . The apparatus of  claim 110  wherein the device for comparing the thus-digitized second frequency spectrum to the combined first frequency spectra and determining from the comparison the condition of the network at the second time together include a device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra. 
   
   
       112 . The apparatus of  claim 111  wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra includes a device for teaching a neural network the combined first frequency spectra. 
   
   
       113 . The apparatus of  claim 112  wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra. 
   
   
       114 . The apparatus of  claim 113  wherein the device for teaching a neural network the combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra. 
   
   
       115 . The apparatus of  claim 110  wherein the device for comparing the second frequency spectrum to the combined first frequency spectra includes a device for digitizing the first frequency spectra. 
   
   
       116 . The apparatus of  claim 115  wherein the device for comparing the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra and determining from the comparison a condition of the network together include a device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       117 . The apparatus of  claim 116  wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra includes a device for teaching a neural network the thus-digitized combined first frequency spectra. 
   
   
       118 . The apparatus of  claim 117  wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       119 . The apparatus of  claim 118  wherein the device for teaching a neural network the thus-digitized combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       120 . The apparatus of  claim 104  wherein the device for comparing the second frequency spectrum to the combined first frequency spectra includes a device for digitizing the combined first frequency spectra. 
   
   
       121 . The apparatus of  claim 120  wherein the device for comparing the second frequency spectrum to the thus-digitized combined first frequency spectra and determining from the comparison a condition of the network together include a device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       122 . The apparatus of  claim 121  wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra includes a device for teaching a neural network the thus-digitized combined first frequency spectra. 
   
   
       123 . The apparatus of  claim 122  wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra. 
   
   
       124 . The apparatus of  claim 123  wherein the device for teaching a neural network the thus-digitized combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra.

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