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A platform for research: civil engineering, architecture and urbanism
DDoS Attacks Detection with Deep Learning Model Using a Cloud Architecture
https://orcid.org/http://orcid.org/0000-0002-1089-4605
https://orcid.org/http://orcid.org/0000-0002-6961-4604
https://orcid.org/http://orcid.org/0000-0002-4608-281X
https://orcid.org/http://orcid.org/0000-0001-8072-9130
https://orcid.org/http://orcid.org/0000-0002-7117-3051
Conventional techniques for the detection of distributed denial-of- service attacks have proven to be insufficient in the face of the variety and mutation of the typology of these anomalous behaviors, likewise, emerging detection and prevention technologies are presented as solutions in workstations and/or in processes not deployed as services. Therefore, this project implements an Intrusion Detection System (IDS) supported by deep learning techniques, with a service-oriented architecture in the cloud for the detection of Distributed Denial of Service (DDoS) attacks, obtaining as a result the improvement of the classification metrics as well as the availability of these resources in an environment deployed as a service.
DDoS Attacks Detection with Deep Learning Model Using a Cloud Architecture
https://orcid.org/http://orcid.org/0000-0002-1089-4605
https://orcid.org/http://orcid.org/0000-0002-6961-4604
https://orcid.org/http://orcid.org/0000-0002-4608-281X
https://orcid.org/http://orcid.org/0000-0001-8072-9130
https://orcid.org/http://orcid.org/0000-0002-7117-3051
Conventional techniques for the detection of distributed denial-of- service attacks have proven to be insufficient in the face of the variety and mutation of the typology of these anomalous behaviors, likewise, emerging detection and prevention technologies are presented as solutions in workstations and/or in processes not deployed as services. Therefore, this project implements an Intrusion Detection System (IDS) supported by deep learning techniques, with a service-oriented architecture in the cloud for the detection of Distributed Denial of Service (DDoS) attacks, obtaining as a result the improvement of the classification metrics as well as the availability of these resources in an environment deployed as a service.
DDoS Attacks Detection with Deep Learning Model Using a Cloud Architecture
https://orcid.org/http://orcid.org/0000-0002-1089-4605
https://orcid.org/http://orcid.org/0000-0002-6961-4604
https://orcid.org/http://orcid.org/0000-0002-4608-281X
https://orcid.org/http://orcid.org/0000-0001-8072-9130
https://orcid.org/http://orcid.org/0000-0002-7117-3051
Conventional techniques for the detection of distributed denial-of- service attacks have proven to be insufficient in the face of the variety and mutation of the typology of these anomalous behaviors, likewise, emerging detection and prevention technologies are presented as solutions in workstations and/or in processes not deployed as services. Therefore, this project implements an Intrusion Detection System (IDS) supported by deep learning techniques, with a service-oriented architecture in the cloud for the detection of Distributed Denial of Service (DDoS) attacks, obtaining as a result the improvement of the classification metrics as well as the availability of these resources in an environment deployed as a service.
DDoS Attacks Detection with Deep Learning Model Using a Cloud Architecture
Lect. Notes in Networks, Syst.
Castillo Ossa, Luis Fernando (editor) / Isaza, Gustavo (editor) / Cardona, Óscar (editor) / Castrillón, Omar Danilo (editor) / Corchado Rodriguez, Juan Manuel (editor) / De la Prieta Pintado, Fernando (editor) / Isaza, Gustavo (author) / Ramirez, Fabian (author) / Duque, Néstor (author) / Lopez, Jeferson Arango (author)
Sustainable Smart Cities and Territories International Conference ; 2023 ; Manizales, Colombia
2023-09-02
10 pages
Article/Chapter (Book)
Electronic Resource
English
DDoS , Deep learning model using a cloud architecture , DDoS attacks detection , Deep neural network , Real-time detection DDoS and DoS Engineering , Computational Intelligence , Transportation Technology and Traffic Engineering , Environmental Policy , Sociology, general , Sustainable Architecture/Green Buildings , Urban Studies/Sociology
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