Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases

dc.creatorMejia Cabrera, Heber I.
dc.creatorPaico Chileno, Daniel
dc.creatorValdera Contreras, Jhon H.
dc.creatorTuesta Monteza, Victor A.
dc.creatorForero, Manuel G.
dc.date2021-11-12T20:45:20Z
dc.date2021-11-12T20:45:20Z
dc.date2021-06-16
dc.date.accessioned2023-08-31T19:24:47Z
dc.date.available2023-08-31T19:24:47Z
dc.identifierMejia-Cabrera H.I., Paico-Chileno D., Valdera-Contreras J.H., Tuesta-Monteza V.A., Forero M.G. (2021) Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases. In: Roman-Rangel E., Kuri-Morales ?.F., Mart?nez-Trinidad J.F., Carrasco-Ochoa J.A., Olvera-L?pez J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science, vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_3
dc.identifier0302-9743
dc.identifierhttps://link.springer.com/chapter/10.1007/978-3-030-77004-4_3
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8558081
dc.descriptionNoSQL 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%.
dc.descriptionUniversidad de Ibagu?
dc.languageen
dc.publisherLecture Notes in Computer Science
dc.subjectClassification
dc.subjectMachine learning
dc.subjectInjection attack
dc.subjectNoSQL
dc.subjectData set construction
dc.subjectJSON
dc.subjectData security
dc.titleAutomatic Detection of Injection Attacks by Machine Learning in NoSQL Databases
dc.typeArticle


Este ítem pertenece a la siguiente institución