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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15760
Title: AL-DDoS attack detection optimized with genetic algorithms
Authors: Quequezana Buendia, Jan Camilo
Santisteban Pablo, Julio Omar
Keywords: Artificial intelligence;Denial-of-service attack;Genetic algorithms;Information services;Network security;Soft computing;Application layers;Configuration parameters;DDOS;Internet information services;Legitimate users;Security;Statistical modeling;Variances analysis;Network layers
Issue Date: 2018
Publisher: Springer Verlag
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059943641&doi=10.1007%2f978-3-030-02837-4_9&partnerID=40&md5=da02e9e708e9db2cbae0666ac1824b78
Abstract: Application Layer DDoS (AL-DDoS) is a major danger for Internet information services, because these attacks are easily performed and implemented by attackers and are difficult to detect and stop using traditional firewalls. Managing to saturate physically and computationally the information services offered on the network. Directly harming legitimate users, to deal with this type of attacks in the network layer previous approaches propose to use a configurable statistical model and observed that when being optimized in various configuration parameters Using Genetic Algorithms was able to optimize the effectiveness to detect Network Layer DDoS (NL-DDoS), however this method is not enough to stop DDoS at the level of application because this level presents different characteristics, that is why we propose a new method Configurable and optimized for different scenarios of Attacks that effectively detect AL-DDoS. © Springer Nature Switzerland AG 2018.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15760
ISSN: 3029743
Appears in Collections:Artículos de investigación

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