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Smart city article details
| Title | Convolutional Neural Network For Ddos Detection |
|---|---|
| ID_Doc | 16104 |
| Authors | Ramirez F.; Isaza G.; Duque N.; Lopez J.A.; Montes J. |
| Year | 2023 |
| Published | Lecture Notes in Networks and Systems, 732 LNNS |
| DOI | http://dx.doi.org/10.1007/978-3-031-36957-5_40 |
| Abstract | 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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords | Convolutional Neural Network for DDoS Detection; DDoS; DDos and Dos attacks in smart cities; DDoS attacks detection |
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