MX2021007883A - Método de predicción del estado de salud de las redes distribuidas mediante redes neurales artificiales. - Google Patents

Método de predicción del estado de salud de las redes distribuidas mediante redes neurales artificiales.

Info

Publication number
MX2021007883A
MX2021007883A MX2021007883A MX2021007883A MX2021007883A MX 2021007883 A MX2021007883 A MX 2021007883A MX 2021007883 A MX2021007883 A MX 2021007883A MX 2021007883 A MX2021007883 A MX 2021007883A MX 2021007883 A MX2021007883 A MX 2021007883A
Authority
MX
Mexico
Prior art keywords
health status
forecasting
actual
artificial neural
phase
Prior art date
Application number
MX2021007883A
Other languages
English (en)
Inventor
Andrea Carcano
Moreno Carullo
Original Assignee
Nozomi Networks Sagl
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nozomi Networks Sagl filed Critical Nozomi Networks Sagl
Publication of MX2021007883A publication Critical patent/MX2021007883A/es

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1433—Vulnerability analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3447—Performance evaluation by modeling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/22—Parsing or analysis of headers

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Business, Economics & Management (AREA)
  • Biophysics (AREA)
  • Software Systems (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • Human Resources & Organizations (AREA)
  • Computer Security & Cryptography (AREA)
  • Strategic Management (AREA)
  • Quality & Reliability (AREA)
  • Economics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computer Hardware Design (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Game Theory and Decision Science (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Educational Administration (AREA)
  • Automation & Control Theory (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La presente invención se refiere a un método para pronosticar el estado de salud de una red distribuida mediante una red neuronal artificial que comprende la fase de identificación de uno o más sitios, uno o más activos de los lados y los enlaces entre los activos identificados en dicha red distribuida, que comprende la fase de evaluación del estado de salud real de cada uno de los activos identificados, la fase de evaluación del estado de salud real de cada uno de dichos sitios identificados y la fase de pronóstico, por la red neuronal artificial, del estado de salud posterior de cada uno de los sitios identificados de acuerdo con una función de pronóstico basada en un conjunto de valores que comprenden el rango de estado de salud real del activo, el riesgo de infección real del activo, el factor de infección real del activo, el rango de estado de salud real del sitio y el riesgo real de infección del sitio.
MX2021007883A 2020-06-29 2021-06-25 Método de predicción del estado de salud de las redes distribuidas mediante redes neurales artificiales. MX2021007883A (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US16/915,326 US11586921B2 (en) 2020-06-29 2020-06-29 Method for forecasting health status of distributed networks by artificial neural networks

Publications (1)

Publication Number Publication Date
MX2021007883A true MX2021007883A (es) 2022-01-18

Family

ID=76695640

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2021007883A MX2021007883A (es) 2020-06-29 2021-06-25 Método de predicción del estado de salud de las redes distribuidas mediante redes neurales artificiales.

Country Status (9)

Country Link
US (1) US11586921B2 (es)
EP (1) EP3934202A1 (es)
JP (1) JP2022013823A (es)
CN (1) CN113934587A (es)
AU (1) AU2021204299A1 (es)
BR (1) BR102021012820A2 (es)
CA (1) CA3123332A1 (es)
MX (1) MX2021007883A (es)
TW (1) TW202201930A (es)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11184385B2 (en) 2018-12-03 2021-11-23 Accenture Global Solutions Limited Generating attack graphs in agile security platforms
US11281806B2 (en) * 2018-12-03 2022-03-22 Accenture Global Solutions Limited Generating attack graphs in agile security platforms
CN115618353B (zh) * 2022-10-21 2024-01-23 北京珞安科技有限责任公司 一种工业生产安全的识别系统及方法

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030115133A1 (en) * 2001-12-13 2003-06-19 Dun & Bradstreet, Inc. Higher risk score for identifying potential illegality in business-to-business relationships
US8359650B2 (en) * 2002-10-01 2013-01-22 Skybox Secutiry Inc. System, method and computer readable medium for evaluating potential attacks of worms
US8239498B2 (en) * 2005-10-28 2012-08-07 Bank Of America Corporation System and method for facilitating the implementation of changes to the configuration of resources in an enterprise
US20080134046A1 (en) * 2006-12-05 2008-06-05 Microsoft Corporation Aggregated computer health
US8595845B2 (en) * 2012-01-19 2013-11-26 Mcafee, Inc. Calculating quantitative asset risk
US9912683B2 (en) * 2013-04-10 2018-03-06 The United States Of America As Represented By The Secretary Of The Army Method and apparatus for determining a criticality surface of assets to enhance cyber defense
US9940467B2 (en) * 2016-06-10 2018-04-10 Optum, Inc. Systems and apparatuses for architecture assessment and policy enforcement
US11063836B2 (en) * 2017-03-21 2021-07-13 Cisco Technology, Inc. Mixing rule-based and machine learning-based indicators in network assurance systems
US10853739B2 (en) * 2017-06-09 2020-12-01 Sap Se Machine learning models for evaluating entities in a high-volume computer network
US11080639B2 (en) * 2018-05-29 2021-08-03 Visa International Service Association Intelligent diversification tool

Also Published As

Publication number Publication date
JP2022013823A (ja) 2022-01-18
CN113934587A (zh) 2022-01-14
BR102021012820A2 (pt) 2022-01-11
AU2021204299A1 (en) 2022-01-20
US11586921B2 (en) 2023-02-21
CA3123332A1 (en) 2021-12-29
US20210406675A1 (en) 2021-12-30
TW202201930A (zh) 2022-01-01
EP3934202A1 (en) 2022-01-05

Similar Documents

Publication Publication Date Title
MX2021007883A (es) Método de predicción del estado de salud de las redes distribuidas mediante redes neurales artificiales.
Claveria et al. A new forecasting approach for the hospitality industry
Wyatt et al. Social concerns, risk and the acceptability of forest vegetation management alternatives: Insights for managers
MY195878A (en) System And Method For Training Neural Networks
Kongaut et al. Unbundling and infrastructure competition for broadband adoption: Implications for NGA regulation
Ighravwe et al. A fuzzy-grey-weighted aggregate sum product assessment methodical approach for multi-criteria analysis of maintenance performance systems
Lim et al. Optimisation of Industry Revolution 4.0 Implementation Strategy for Palm Oil Industry in Cyber Security.
EP3742355A3 (en) Quantum recommendation system
Kaviani et al. Supply chain resilience assessment: a grey systems theory approach
Ponnambalam et al. Decision trees to model the impact of disruption and recovery in supply chain networks
Gilissen The integration of the adaptation approach into EU and Dutch legislation on flood risk management
Xie et al. Time-dependent series system reliability analysis method and application to gear transmission systems
Muralidharan et al. Stochastic hourly load forecasting for smart grids in korea using narx model
Pandey et al. Use of data driven techniques for short lead time streamflow forecasting in Mahanadi Basin
Gomes et al. Time series forecasting with neural networks and choquet integral
Gladysz et al. An assessment of technologies with wide range of impact. A case of RFID
Umamaheswari et al. Stochastic model based reliability centered preventive generator maintenance planning using Ant Lion Optimizer
Kozik Application of multi-criteria decision analysis in selecting of sustainable investments
Zoucas et al. An empirical analysis of a neural network model for the time series forecasting of different industrial segments
Prudêncio et al. LearningWeights for linear combination of forecasting methods
GHAZALI et al. Dynamic Ridge Polynomial Neural Network with a Real Time Recurrent Learning Algorithm: Forecasting the S&P 500 (< Special Issue> SOFT COMPUTING METHODOLOGIES AND ITS APPLICATIONS)
Graham et al. Strengthening urban landscape planning: a metapopulation modelling framework
Padhi et al. A trigonometric Functional link single layer Neural Network approach for non linear dynamic plant identification using second order Levenberg-Marquardt Algorithm
Takahashi et al. Relations between partial diversity and organizational performance in an organization
Gunawardana et al. Quantitative measurement of advanced manufacturing technology transfer from foreign-based companies to local companies