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Convolutional Neural Network for DDoS Detection
https://orcid.org/http://orcid.org/0000-0002-6961-4604
https://orcid.org/http://orcid.org/0000-0002-1089-4605
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
A DoS attack is a sort of cyber assault in which an attacker attempts to overload or disable a network, server, or application by flooding it with traffic or overloading it with requests. In addition, the implementation of intrusion detection systems based on convolutional neural network help to detect and respond to attacks in real time before it is too late by knowing the importance of critical services and infrastructures present in a smart city. A CNN may detect malign traffic patterns and warn by monitoring the frequency and amount of incoming traffic. Therefore, this paper implements a denial of service attack detection system under the architecture of a convolutional neural network that demonstrates the effectiveness of detection of the Dos attacks with an accuracy of 98.39% f1 score of 98.43%, and Precision of 98.39%, which is very important to provide security to critical infrastructures in smart cities.
Convolutional Neural Network for DDoS Detection
https://orcid.org/http://orcid.org/0000-0002-6961-4604
https://orcid.org/http://orcid.org/0000-0002-1089-4605
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
A DoS attack is a sort of cyber assault in which an attacker attempts to overload or disable a network, server, or application by flooding it with traffic or overloading it with requests. In addition, the implementation of intrusion detection systems based on convolutional neural network help to detect and respond to attacks in real time before it is too late by knowing the importance of critical services and infrastructures present in a smart city. A CNN may detect malign traffic patterns and warn by monitoring the frequency and amount of incoming traffic. Therefore, this paper implements a denial of service attack detection system under the architecture of a convolutional neural network that demonstrates the effectiveness of detection of the Dos attacks with an accuracy of 98.39% f1 score of 98.43%, and Precision of 98.39%, which is very important to provide security to critical infrastructures in smart cities.
Convolutional Neural Network for DDoS Detection
https://orcid.org/http://orcid.org/0000-0002-6961-4604
https://orcid.org/http://orcid.org/0000-0002-1089-4605
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
A DoS attack is a sort of cyber assault in which an attacker attempts to overload or disable a network, server, or application by flooding it with traffic or overloading it with requests. In addition, the implementation of intrusion detection systems based on convolutional neural network help to detect and respond to attacks in real time before it is too late by knowing the importance of critical services and infrastructures present in a smart city. A CNN may detect malign traffic patterns and warn by monitoring the frequency and amount of incoming traffic. Therefore, this paper implements a denial of service attack detection system under the architecture of a convolutional neural network that demonstrates the effectiveness of detection of the Dos attacks with an accuracy of 98.39% f1 score of 98.43%, and Precision of 98.39%, which is very important to provide security to critical infrastructures in smart cities.
Convolutional Neural Network for DDoS Detection
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) / Ramirez, Fabian (author) / Isaza, Gustavo (author) / Duque, Néstor (author) / Lopez, Jeferson Arango (author)
Sustainable Smart Cities and Territories International Conference ; 2023 ; Manizales, Colombia
2023-09-02
7 pages
Article/Chapter (Book)
Electronic Resource
English
DDoS , DDos and Dos attacks in smart cities , DDoS attacks detection , Convolutional Neural Network for DDoS Detection Engineering , Computational Intelligence , Transportation Technology and Traffic Engineering , Environmental Policy , Sociology, general , Sustainable Architecture/Green Buildings , Urban Studies/Sociology
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