Using Quadratic Discriminant Analysis by Intrusion Detection Systems for Port Scan and Slowloris Attack Classification

Autor:	Bruno Lopes Dalmazo, Luciano Paschoal Gaspary, Jéferson Campos Nobre, Lisandro Zambenedetti Granville, Luiz Ricardo Bertoldi de Oliveira, Vinícius M. Deolindo, Marcus Vinicius Brito da Silva, Allan de Barcelos Silva
Rok vydání:	2021
Předmět:	Constant (computer programming) Slowloris Computer science Classifier (linguistics) Process (computing) Denial-of-service attack Intrusion detection system Data mining Quadratic classifier computer.software_genre Port scan computer
Zdroj:	Computational Science and Its Applications – ICCSA 2021 ISBN: 9783030869694 ICCSA (3)
DOI:	10.1007/978-3-030-86970-0_14
Popis:	Identify and classify attacks through Intrusion Detection Systems is one constant challenge for security professionals. Computer networks are one of the significant IT components that support classification operations. Machine Learning (ML) techniques can aid in this process by providing methods capable of making decisions based on previously known information. In light of this, literature shows that Quadratic Discriminant Analysis (QDA) is barely explored as a classification method for IDS. To fill this gap, this study aims to create a new classifier able to distinguish legitimate network traffic from an attack by adopting ML techniques and QDA algorithms for identifying Port Scan and DoS Slowloris attacks.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::194f6b92657aa11344b791abe95ff281 https://doi.org/10.1007/978-3-030-86970-0_14 Zobrazit plný text záznamu