Learning classifier systems for adaptive learning of intrusion detection system

Chang Seok Lee; Sung Bae Cho

doi:10.1007/978-3-319-67180-2_54

Learning classifier systems for adaptive learning of intrusion detection system

Chang Seok Lee, Sung Bae Cho

College of Computing

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

Abstract

Relational databases contain information that must be protected such as personal information, the problem of intrusion detection of relational database is considered important. Also, the pattern of attacks evolves and it is difficult to grasp by rule-based method or general machine learning, so adaptive learning is needed. Learning classifier systems are system that combines supervised learning, reinforcement learning and evolutionary computation. It creates and updates classifiers according to data input. Learning classifier systems can learn adaptive because they generate and evaluate classifiers in real time. In this paper, we apply accuracy based learning classifier systems to relational database and confirm that adaptive learning is possible. Also, we confirmed their practical usability that they close to the best accuracy, though were not the best.

Original language	English
Title of host publication	International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings
Editors	Hilde Perez Garcia, Javier Alfonso-Cendon, Lidia Sanchez Gonzalez, Emilio Corchado, Hector Quintian
Publisher	Springer Verlag
Pages	557-566
Number of pages	10
ISBN (Print)	9783319671796
DOIs	https://doi.org/10.1007/978-3-319-67180-2_54
Publication status	Published - 2018
Event	International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017 - Leon, Spain Duration: 2017 Sept 6 → 2017 Sept 8

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	649
ISSN (Print)	2194-5357

Other

Other	International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017
Country/Territory	Spain
City	Leon
Period	17/9/6 → 17/9/8

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract. (UD160066BD).

Publisher Copyright:
© 2018, Springer International Publishing AG.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Computer Science(all)

Access to Document

10.1007/978-3-319-67180-2_54

Cite this

Lee, C. S., & Cho, S. B. (2018). Learning classifier systems for adaptive learning of intrusion detection system. In H. Perez Garcia, J. Alfonso-Cendon, L. Sanchez Gonzalez, E. Corchado, & H. Quintian (Eds.), International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings (pp. 557-566). (Advances in Intelligent Systems and Computing; Vol. 649). Springer Verlag. https://doi.org/10.1007/978-3-319-67180-2_54

Lee, Chang Seok ; Cho, Sung Bae. / Learning classifier systems for adaptive learning of intrusion detection system. International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. editor / Hilde Perez Garcia ; Javier Alfonso-Cendon ; Lidia Sanchez Gonzalez ; Emilio Corchado ; Hector Quintian. Springer Verlag, 2018. pp. 557-566 (Advances in Intelligent Systems and Computing).

@inproceedings{b6e8a094f13c4101924cbd85828292f5,

title = "Learning classifier systems for adaptive learning of intrusion detection system",

abstract = "Relational databases contain information that must be protected such as personal information, the problem of intrusion detection of relational database is considered important. Also, the pattern of attacks evolves and it is difficult to grasp by rule-based method or general machine learning, so adaptive learning is needed. Learning classifier systems are system that combines supervised learning, reinforcement learning and evolutionary computation. It creates and updates classifiers according to data input. Learning classifier systems can learn adaptive because they generate and evaluate classifiers in real time. In this paper, we apply accuracy based learning classifier systems to relational database and confirm that adaptive learning is possible. Also, we confirmed their practical usability that they close to the best accuracy, though were not the best.",

author = "Lee, {Chang Seok} and Cho, {Sung Bae}",

note = "Funding Information: Acknowledgements. This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract. (UD160066BD). Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG.; International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017 ; Conference date: 06-09-2017 Through 08-09-2017",

year = "2018",

doi = "10.1007/978-3-319-67180-2_54",

language = "English",

isbn = "9783319671796",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "557--566",

editor = "{Perez Garcia}, Hilde and Javier Alfonso-Cendon and {Sanchez Gonzalez}, Lidia and Emilio Corchado and Hector Quintian",

booktitle = "International Joint Conference SOCO{\textquoteright}17- CISIS{\textquoteright}17-ICEUTE{\textquoteright}17, Proceedings",

address = "Germany",

}

Lee, CS & Cho, SB 2018, Learning classifier systems for adaptive learning of intrusion detection system. in H Perez Garcia, J Alfonso-Cendon, L Sanchez Gonzalez, E Corchado & H Quintian (eds), International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. Advances in Intelligent Systems and Computing, vol. 649, Springer Verlag, pp. 557-566, International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017, Leon, Spain, 17/9/6. https://doi.org/10.1007/978-3-319-67180-2_54

Learning classifier systems for adaptive learning of intrusion detection system. / Lee, Chang Seok; Cho, Sung Bae.
International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. ed. / Hilde Perez Garcia; Javier Alfonso-Cendon; Lidia Sanchez Gonzalez; Emilio Corchado; Hector Quintian. Springer Verlag, 2018. p. 557-566 (Advances in Intelligent Systems and Computing; Vol. 649).

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

TY - GEN

T1 - Learning classifier systems for adaptive learning of intrusion detection system

AU - Lee, Chang Seok

AU - Cho, Sung Bae

N1 - Funding Information: Acknowledgements. This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract. (UD160066BD). Publisher Copyright: © 2018, Springer International Publishing AG.

PY - 2018

Y1 - 2018

N2 - Relational databases contain information that must be protected such as personal information, the problem of intrusion detection of relational database is considered important. Also, the pattern of attacks evolves and it is difficult to grasp by rule-based method or general machine learning, so adaptive learning is needed. Learning classifier systems are system that combines supervised learning, reinforcement learning and evolutionary computation. It creates and updates classifiers according to data input. Learning classifier systems can learn adaptive because they generate and evaluate classifiers in real time. In this paper, we apply accuracy based learning classifier systems to relational database and confirm that adaptive learning is possible. Also, we confirmed their practical usability that they close to the best accuracy, though were not the best.

AB - Relational databases contain information that must be protected such as personal information, the problem of intrusion detection of relational database is considered important. Also, the pattern of attacks evolves and it is difficult to grasp by rule-based method or general machine learning, so adaptive learning is needed. Learning classifier systems are system that combines supervised learning, reinforcement learning and evolutionary computation. It creates and updates classifiers according to data input. Learning classifier systems can learn adaptive because they generate and evaluate classifiers in real time. In this paper, we apply accuracy based learning classifier systems to relational database and confirm that adaptive learning is possible. Also, we confirmed their practical usability that they close to the best accuracy, though were not the best.

UR - http://www.scopus.com/inward/record.url?scp=85028680808&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028680808&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-67180-2_54

DO - 10.1007/978-3-319-67180-2_54

M3 - Conference contribution

AN - SCOPUS:85028680808

SN - 9783319671796

T3 - Advances in Intelligent Systems and Computing

SP - 557

EP - 566

BT - International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings

A2 - Perez Garcia, Hilde

A2 - Alfonso-Cendon, Javier

A2 - Sanchez Gonzalez, Lidia

A2 - Corchado, Emilio

A2 - Quintian, Hector

PB - Springer Verlag

T2 - International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017

Y2 - 6 September 2017 through 8 September 2017

ER -

Lee CS, Cho SB. Learning classifier systems for adaptive learning of intrusion detection system. In Perez Garcia H, Alfonso-Cendon J, Sanchez Gonzalez L, Corchado E, Quintian H, editors, International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. Springer Verlag. 2018. p. 557-566. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-67180-2_54

Learning classifier systems for adaptive learning of intrusion detection system

Abstract

Publication series

Other

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this