Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack

Seok Jun Bu; Sung Bae Cho

doi:10.1007/978-3-030-29859-3_13

Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack

Seok Jun Bu, Sung Bae Cho

College of Computing

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

3 Citations (Scopus)

Abstract

A database Intrusion Detection System (IDS) based on Role-based Access Control (RBAC) mechanism that has capability of learning and adaptation learns SQL transaction patterns represented by roles to detect insider attacks. In this paper, we parameterize the rules for partitioning the entire query set into multiple areas with simple chromosomes and propose an ensemble of multiple deep learning models that can effectively model the tree structural characteristics of SQL transactions. Experimental results on a large synthetic query dataset verify that it quantitatively outperforms other ensemble methods and machine learning methods including deep learning models, in terms of 10-fold cross validation and chi-square validation.

Original language	English
Title of host publication	Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings
Editors	Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Corchado Rodríguez
Publisher	Springer Verlag
Pages	145-156
Number of pages	12
ISBN (Print)	9783030298586
DOIs	https://doi.org/10.1007/978-3-030-29859-3_13
Publication status	Published - 2019
Event	14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019 - León, Spain Duration: 2019 Sept 4 → 2019 Sept 6

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11734 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019
Country/Territory	Spain
City	León
Period	19/9/4 → 19/9/6

Bibliographical note

Funding Information:
This research was supported by Korea Electric Power Corporation. (Grant number: R18XA05).

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-29859-3_13

Cite this

Bu, S. J., & Cho, S. B. (2019). Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack. In H. Pérez García, L. Sánchez González, M. Castejón Limas, H. Quintián Pardo, & E. Corchado Rodríguez (Eds.), Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings (pp. 145-156). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11734 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-29859-3_13

Bu, Seok Jun ; Cho, Sung Bae. / Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack. Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings. editor / Hilde Pérez García ; Lidia Sánchez González ; Manuel Castejón Limas ; Héctor Quintián Pardo ; Emilio Corchado Rodríguez. Springer Verlag, 2019. pp. 145-156 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack",

abstract = "A database Intrusion Detection System (IDS) based on Role-based Access Control (RBAC) mechanism that has capability of learning and adaptation learns SQL transaction patterns represented by roles to detect insider attacks. In this paper, we parameterize the rules for partitioning the entire query set into multiple areas with simple chromosomes and propose an ensemble of multiple deep learning models that can effectively model the tree structural characteristics of SQL transactions. Experimental results on a large synthetic query dataset verify that it quantitatively outperforms other ensemble methods and machine learning methods including deep learning models, in terms of 10-fold cross validation and chi-square validation.",

author = "Bu, {Seok Jun} and Cho, {Sung Bae}",

note = "Funding Information: This research was supported by Korea Electric Power Corporation. (Grant number: R18XA05). Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019 ; Conference date: 04-09-2019 Through 06-09-2019",

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doi = "10.1007/978-3-030-29859-3_13",

language = "English",

isbn = "9783030298586",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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editor = "{P{\'e}rez Garc{\'i}a}, Hilde and {S{\'a}nchez Gonz{\'a}lez}, Lidia and {Castej{\'o}n Limas}, Manuel and {Quinti{\'a}n Pardo}, H{\'e}ctor and {Corchado Rodr{\'i}guez}, Emilio",

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Bu, SJ & Cho, SB 2019, Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack. in H Pérez García, L Sánchez González, M Castejón Limas, H Quintián Pardo & E Corchado Rodríguez (eds), Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11734 LNAI, Springer Verlag, pp. 145-156, 14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019, León, Spain, 19/9/4. https://doi.org/10.1007/978-3-030-29859-3_13

Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack. / Bu, Seok Jun; Cho, Sung Bae.

Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings. ed. / Hilde Pérez García; Lidia Sánchez González; Manuel Castejón Limas; Héctor Quintián Pardo; Emilio Corchado Rodríguez. Springer Verlag, 2019. p. 145-156 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11734 LNAI).

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

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AU - Cho, Sung Bae

PY - 2019

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N2 - A database Intrusion Detection System (IDS) based on Role-based Access Control (RBAC) mechanism that has capability of learning and adaptation learns SQL transaction patterns represented by roles to detect insider attacks. In this paper, we parameterize the rules for partitioning the entire query set into multiple areas with simple chromosomes and propose an ensemble of multiple deep learning models that can effectively model the tree structural characteristics of SQL transactions. Experimental results on a large synthetic query dataset verify that it quantitatively outperforms other ensemble methods and machine learning methods including deep learning models, in terms of 10-fold cross validation and chi-square validation.

AB - A database Intrusion Detection System (IDS) based on Role-based Access Control (RBAC) mechanism that has capability of learning and adaptation learns SQL transaction patterns represented by roles to detect insider attacks. In this paper, we parameterize the rules for partitioning the entire query set into multiple areas with simple chromosomes and propose an ensemble of multiple deep learning models that can effectively model the tree structural characteristics of SQL transactions. Experimental results on a large synthetic query dataset verify that it quantitatively outperforms other ensemble methods and machine learning methods including deep learning models, in terms of 10-fold cross validation and chi-square validation.

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

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BT - Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings

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A2 - Castejón Limas, Manuel

A2 - Quintián Pardo, Héctor

A2 - Corchado Rodríguez, Emilio

PB - Springer Verlag

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Y2 - 4 September 2019 through 6 September 2019

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Bu SJ, Cho SB. Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack. In Pérez García H, Sánchez González L, Castejón Limas M, Quintián Pardo H, Corchado Rodríguez E, editors, Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings. Springer Verlag. 2019. p. 145-156. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-29859-3_13

Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

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