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

doi:10.1007/978-3-030-77004-4_3

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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%.

Original language	English
Title of host publication	Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
Editors	Edgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	23-32
Number of pages	10
ISBN (Print)	9783030770037
DOIs	https://doi.org/10.1007/978-3-030-77004-4_3
State	Published - 2021
Externally published	Yes
Event	13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online Duration: 23 Jun 2021 → 26 Jun 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12725 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Mexican Conference on Pattern Recognition, MCPR 2021
City	Virtual, Online
Period	23/06/21 → 26/06/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Classification
Data security
Data set construction
Injection attack
JSON
Machine learning
NoSQL

Access to Document

10.1007/978-3-030-77004-4_3

Cite this

Mejia-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 E. Roman-Rangel, Á. F. Kuri-Morales, J. F. Martínez-Trinidad, J. A. Carrasco-Ochoa, & J. A. Olvera-López (Eds.), Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings (pp. 23-32). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12725 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-77004-4_3

Mejia-Cabrera, Heber I. ; Paico-Chileno, Daniel ; Valdera-Contreras, Jhon H. et al. / Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases. Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings. editor / Edgar Roman-Rangel ; Ángel Fernando Kuri-Morales ; José Francisco Martínez-Trinidad ; Jesús Ariel Carrasco-Ochoa ; José Arturo Olvera-López. Springer Science and Business Media Deutschland GmbH, 2021. pp. 23-32 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases",

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%.",

keywords = "Classification, Data security, Data set construction, Injection attack, JSON, Machine learning, NoSQL",

author = "Mejia-Cabrera, {Heber I.} and Daniel Paico-Chileno and Valdera-Contreras, {Jhon H.} and Tuesta-Monteza, {Victor A.} and Forero, {Manuel G.}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 13th Mexican Conference on Pattern Recognition, MCPR 2021 ; Conference date: 23-06-2021 Through 26-06-2021",

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Mejia-Cabrera, HI, Paico-Chileno, D, Valdera-Contreras, JH, Tuesta-Monteza, VA & Forero, MG 2021, Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases. in E Roman-Rangel, ÁF Kuri-Morales, JF Martínez-Trinidad, JA Carrasco-Ochoa & JA Olvera-López (eds), Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12725 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 23-32, 13th Mexican Conference on Pattern Recognition, MCPR 2021, Virtual, Online, 23/06/21. https://doi.org/10.1007/978-3-030-77004-4_3

Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases. / Mejia-Cabrera, Heber I.; Paico-Chileno, Daniel; Valdera-Contreras, Jhon H. et al.
Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings. ed. / Edgar Roman-Rangel; Ángel Fernando Kuri-Morales; José Francisco Martínez-Trinidad; Jesús Ariel Carrasco-Ochoa; José Arturo Olvera-López. Springer Science and Business Media Deutschland GmbH, 2021. p. 23-32 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12725 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases

AU - Mejia-Cabrera, Heber I.

AU - Paico-Chileno, Daniel

AU - Valdera-Contreras, Jhon H.

AU - Tuesta-Monteza, Victor A.

AU - Forero, Manuel G.

PY - 2021

Y1 - 2021

N2 - 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%.

AB - 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%.

KW - Classification

KW - Data security

KW - Data set construction

KW - Injection attack

KW - JSON

KW - Machine learning

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DO - 10.1007/978-3-030-77004-4_3

M3 - Contribución a la conferencia

AN - SCOPUS:85111359980

SN - 9783030770037

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings

A2 - Roman-Rangel, Edgar

A2 - Kuri-Morales, Ángel Fernando

A2 - Martínez-Trinidad, José Francisco

A2 - Carrasco-Ochoa, Jesús Ariel

A2 - Olvera-López, José Arturo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 13th Mexican Conference on Pattern Recognition, MCPR 2021

Y2 - 23 June 2021 through 26 June 2021

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Mejia-Cabrera HI, Paico-Chileno D, Valdera-Contreras JH, Tuesta-Monteza VA, Forero MG. Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases. In Roman-Rangel E, Kuri-Morales ÁF, Martínez-Trinidad JF, Carrasco-Ochoa JA, Olvera-López JA, editors, Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 23-32. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-77004-4_3

Automatic Detection of Injection Attacks by Machine Learning in NoSQL Databases

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this