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

Seok Jun Bu, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationHybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings
EditorsHilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Corchado Rodríguez
PublisherSpringer Verlag
Pages145-156
Number of pages12
ISBN (Print)9783030298586
DOIs
Publication statusPublished - 2019 Jan 1
Event14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019 - León, Spain
Duration: 2019 Sep 42019 Sep 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11734 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019
CountrySpain
CityLeón
Period19/9/419/9/6

Fingerprint

Ensemble Learning
Genetic algorithms
Attack
Genetic Algorithm
Transactions
Query
Role-based Access Control
Ensemble Methods
Parameterise
Chi-square
Intrusion detection
Intrusion Detection
Chromosomes
Access control
Cross-validation
Chromosome
Learning systems
Partitioning
Machine Learning
Ensemble

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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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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.",
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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 proceedingConference contribution

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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)). https://doi.org/10.1007/978-3-030-29859-3_13