Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases

Heber I. Mejia-Cabrera, Daniel Paico-Chileno, Jhon H. Valdera-Contreras, Victor A. Tuesta-Monteza, Manuel G. Forero

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

NoSQL databases were created for the purpose of manipulating large amounts of data in real time. However, at the beginning, security was not important for their developers. The popularity of SQL generated the false belief that NoSQL databases were immune to injection attacks. As a consequence, NoSQL databases were not protected and are vulnerable to injection attacks. In addition, databases with NoSQL queries are not available for experimentation. Therefore, this paper presents a new method for the construction of a NoSQL query database, based on JSON structure. Six classification algorithms were evaluated to identify the injection attacks: SVM, Decision Tree, Random Forest, K-NN, Neural Network and Multilayer Perceptron, obtaining an accuracy with the last two algorithms of 97.6%.

Idioma originalInglés
Título de la publicación alojadaPattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
EditoresEdgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas23-32
Número de páginas10
ISBN (versión impresa)9783030770037
DOI
EstadoPublicada - 2021
Publicado de forma externa
Evento13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online
Duración: 23 jun. 202126 jun. 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12725 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia13th Mexican Conference on Pattern Recognition, MCPR 2021
CiudadVirtual, Online
Período23/06/2126/06/21

Nota bibliográfica

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

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