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Título
Proposing to use artificial neural Networks for NoSQL attack detection
Autor(es)
Materia
Security
Attack detection
NoSQL injection
Big data
Feature extraction
Deep learning
Artificial neural network
Clasificación UNESCO
1203.04 Inteligencia Artificial
Fecha de publicación
2021
Editor
Springer
Citación
Alizadehsani, Z.(2021). Proposing to use artificial neural Networks for NoSQL attack detection. Avances en sistemas inteligentes y computación, vol 1242. Springer, Cham.
Serie / N.º
AISC;1242
Resumen
[EN] Relationships databases have enjoyed a certain boom in software
worlds until now. These days, with the rise of modern applications, unstructured
data production, traditional databases do not completely meet the needs of all
systems. Regarding these issues, NOSQL databases have been developed and
are a good alternative. But security aspects stay behind. Injection attacks are the
most serious class of web attacks that are not taken seriously in NoSQL.
This paper presents a Neural Network model approach for NoSQL injection.
This method attempts to use the best and most effective features to identify an
injection. The features used are divided into two categories, the first one based
on the content of the request, and the second one independent of the request
meta parameters. In order to detect attack payloads features, we work on
character level analysis to obtain malicious rate of user inputs. The results
demonstrate that our model has detected more attack payloads compare with
models that work black list approach in keyword level.
URI
ISBN
978-3-030-53828-6
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