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handle: 10256/22676
La robustesa d’una xarxa de transport (telecomunicacions, aigua, electricitat, etc) ens dona un indicatiu per saber quina resistència tenen front diversos tipus d’atac (atacs dirigits, aleatoris, en cascada, etc.). Per calcular aquesta mètrica es necessita d’uns càlculs computacionalment costosos. En aquest TFG s’estudiaran i aplicaran diverses tècniques d’aprenentatge supervisat amb l’objectiu d’obtenuir un model que pugui pronosticar el valor de la robustesa d’una xarxa
The robustness of a transport network (telecommunications, water, electricity, etc) gives us an indicator to know what resistance different types have of attack (targeted, random, cascading attacks, etc.). To calculate this metric computationally expensive calculations are needed. In this TFG, various supervised learning techniques will be studied and applied with the objective to obtain a model that can predict the value of the robustness of a network
Artificial intelligence, Xarxes punt a punt (Xarxes d'ordinadors), Peer-to-peer architecture (Computer networks), Intel·ligència artificial
Artificial intelligence, Xarxes punt a punt (Xarxes d'ordinadors), Peer-to-peer architecture (Computer networks), Intel·ligència artificial
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