Applying the rainbow architecture to intrusion detection systems

There is a lot of expectation on how Artificial Intelligence (AI) is going to have an impact on Cybersecurity. From new sophisticated attacks to new ways of defending a system from cybercriminals. A lot of techniques are being studied by universities and security organizations in order to implement...

ver descrição completa

Detalhes bibliográficos
Autor: Izquierdo García-Faria, Tomás
Formato: tesis de maestría
Fecha de publicación:2021
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/348194
Acesso em linha:https://hdl.handle.net/2117/348194
Access Level:acceso abierto
Palavra-chave:Computer security
Deep learning
Reinforcement learning
Ciberseguretat
Aprenentatge profund de reforç
Sistemes de detecció d'intrusos
IDS
Aprenentatge de reforç
Aprenentatge Q
Rainbow Aprenentatge automàtic
Cybersecurity
Deep Reinforcement Learning
Intrusion Detection Systems
Reinforcement Learning
Q Learning
Rainbow
Machine Learning
Seguretat informàtica
Aprenentatge profund
Aprenentatge per reforç
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
id ES_092ef5bc4e01fe73d7285a623d535547
oai_identifier_str oai:upcommons.upc.edu:2117/348194
network_acronym_str ES
network_name_str España
repository_id_str
spelling Applying the rainbow architecture to intrusion detection systemsDeep reinforcement learning in cybersecurityIzquierdo García-Faria, TomásComputer securityDeep learningReinforcement learningCiberseguretatAprenentatge profund de reforçSistemes de detecció d'intrusosIDSAprenentatge de reforçAprenentatge QRainbow Aprenentatge automàticCybersecurityDeep Reinforcement LearningIntrusion Detection SystemsReinforcement LearningQ LearningRainbowMachine LearningSeguretat informàticaAprenentatge profundAprenentatge per reforçÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialThere is a lot of expectation on how Artificial Intelligence (AI) is going to have an impact on Cybersecurity. From new sophisticated attacks to new ways of defending a system from cybercriminals. A lot of techniques are being studied by universities and security organizations in order to implement intelligent systems to defend systems, as well as researching how can AI give solution to many problems not yet solved. However this sector has some limitations for AI to have a real impact. AI is a discipline which, in its machine learning part, needs data in order to give good results and big companies are not willing to admit there attacks or do not even know they are under attack. In particular network intrusion is one of the most common ways attackers enter a system and exploit its threats. With the adoption of IoT systems in our society more and more, companies, cities, and citizens, are going to fill their surroundings with devices that communicate to the exterior. Under these emerging threats every system and network they are connected to have to be secure and should be able to detect intrusion. This thesis will try to present a conjunction of different techniques, presented by their authors as the Rainbow architecture, used in reinforcement learning in order to tackle this problem. It will give solution to systems such as intrusion detection systems (IDS) and can be extended to intrusion prevention systems (IPS) or web application firewalls (WAF).Universitat Politècnica de CatalunyaVázquez Salceda, JavierGibert, Karina20212021-04-2820212021-07-01master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/348194reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3481942026-05-27T15:37:01Z
dc.title.none.fl_str_mv Applying the rainbow architecture to intrusion detection systems
Deep reinforcement learning in cybersecurity
title Applying the rainbow architecture to intrusion detection systems
spellingShingle Applying the rainbow architecture to intrusion detection systems
Izquierdo García-Faria, Tomás
Computer security
Deep learning
Reinforcement learning
Ciberseguretat
Aprenentatge profund de reforç
Sistemes de detecció d'intrusos
IDS
Aprenentatge de reforç
Aprenentatge Q
Rainbow Aprenentatge automàtic
Cybersecurity
Deep Reinforcement Learning
Intrusion Detection Systems
Reinforcement Learning
Q Learning
Rainbow
Machine Learning
Seguretat informàtica
Aprenentatge profund
Aprenentatge per reforç
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Applying the rainbow architecture to intrusion detection systems
title_full Applying the rainbow architecture to intrusion detection systems
title_fullStr Applying the rainbow architecture to intrusion detection systems
title_full_unstemmed Applying the rainbow architecture to intrusion detection systems
title_sort Applying the rainbow architecture to intrusion detection systems
dc.creator.none.fl_str_mv Izquierdo García-Faria, Tomás
author Izquierdo García-Faria, Tomás
author_facet Izquierdo García-Faria, Tomás
author_role author
dc.contributor.none.fl_str_mv Vázquez Salceda, Javier
Gibert, Karina
dc.subject.none.fl_str_mv Computer security
Deep learning
Reinforcement learning
Ciberseguretat
Aprenentatge profund de reforç
Sistemes de detecció d'intrusos
IDS
Aprenentatge de reforç
Aprenentatge Q
Rainbow Aprenentatge automàtic
Cybersecurity
Deep Reinforcement Learning
Intrusion Detection Systems
Reinforcement Learning
Q Learning
Rainbow
Machine Learning
Seguretat informàtica
Aprenentatge profund
Aprenentatge per reforç
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Computer security
Deep learning
Reinforcement learning
Ciberseguretat
Aprenentatge profund de reforç
Sistemes de detecció d'intrusos
IDS
Aprenentatge de reforç
Aprenentatge Q
Rainbow Aprenentatge automàtic
Cybersecurity
Deep Reinforcement Learning
Intrusion Detection Systems
Reinforcement Learning
Q Learning
Rainbow
Machine Learning
Seguretat informàtica
Aprenentatge profund
Aprenentatge per reforç
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description There is a lot of expectation on how Artificial Intelligence (AI) is going to have an impact on Cybersecurity. From new sophisticated attacks to new ways of defending a system from cybercriminals. A lot of techniques are being studied by universities and security organizations in order to implement intelligent systems to defend systems, as well as researching how can AI give solution to many problems not yet solved. However this sector has some limitations for AI to have a real impact. AI is a discipline which, in its machine learning part, needs data in order to give good results and big companies are not willing to admit there attacks or do not even know they are under attack. In particular network intrusion is one of the most common ways attackers enter a system and exploit its threats. With the adoption of IoT systems in our society more and more, companies, cities, and citizens, are going to fill their surroundings with devices that communicate to the exterior. Under these emerging threats every system and network they are connected to have to be secure and should be able to detect intrusion. This thesis will try to present a conjunction of different techniques, presented by their authors as the Rainbow architecture, used in reinforcement learning in order to tackle this problem. It will give solution to systems such as intrusion detection systems (IDS) and can be extended to intrusion prevention systems (IPS) or web application firewalls (WAF).
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-04-28
2021
2021-07-01
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/348194
url https://hdl.handle.net/2117/348194
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403091193823232
score 15,300719

Registos relacionados