Title
Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases
Date Issued
01 January 2021
Access level
metadata only access
Resource Type
conference paper
Author(s)
Paico-Chileno D.
Valdera-Contreras J.H.
Forero M.G.
Publisher(s)
Springer Science and Business Media Deutschland GmbH
Abstract
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%.
Start page
23
End page
32
Volume
12725 LNCS
Language
English
OCDE Knowledge area
Ciencias de la computación
Subjects
Scopus EID
2-s2.0-85111359980
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Resource of which it is part
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN of the container
03029743
ISBN of the container
978-303077003-7
Conference
13th Mexican Conference on Pattern Recognition, MCPR 2021
Sources of information:
Directorio de Producción Científica
Scopus