Applying accuracy-based LCS to detecting anomalous database access

Suin Seo; Sung Bae Cho

doi:10.1145/3205651.3208315

Applying accuracy-based LCS to detecting anomalous database access

Suin Seo, Sung Bae Cho

Department of Computer Science

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

1 Citation (Scopus)

Abstract

Database intrusion detection (DB-IDS) is the problem of detecting anomalous queries in transaction systems like e-commerce platform. The adaptive detection algorithm is necessary to find anomaly accesses when the environment changes continuously. To solve this problem, we used accuracy-based LCS (XCS), one of the primary model of adaptive machine learning method, for detecting malicious accesses in databases. In the problem of database intrusion detection which changes the detecting targets, we found and analyzed the patterns of rule generation to show systemically how the adaptive learning of XCS algorithm is working in practical usage.

Original language	English
Title of host publication	GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
Publisher	Association for Computing Machinery, Inc
Pages	1442-1448
Number of pages	7
ISBN (Electronic)	9781450357647
DOIs	https://doi.org/10.1145/3205651.3208315
Publication status	Published - 2018 Jul 6
Event	2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan Duration: 2018 Jul 15 → 2018 Jul 19

Publication series

Name	GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Other

Other	2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Country	Japan
City	Kyoto
Period	18/7/15 → 18/7/19

All Science Journal Classification (ASJC) codes

Computer Science Applications
Software
Computational Theory and Mathematics
Theoretical Computer Science

Access to Document

10.1145/3205651.3208315

Fingerprint Dive into the research topics of 'Applying accuracy-based LCS to detecting anomalous database access'. Together they form a unique fingerprint.

Cite this

Seo, S., & Cho, S. B. (2018). Applying accuracy-based LCS to detecting anomalous database access. In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 1442-1448). (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3205651.3208315

Seo, Suin ; Cho, Sung Bae. / Applying accuracy-based LCS to detecting anomalous database access. GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2018. pp. 1442-1448 (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion).

@inproceedings{3ebd770801264be7a8e212eb28fa2add,

title = "Applying accuracy-based LCS to detecting anomalous database access",

abstract = "Database intrusion detection (DB-IDS) is the problem of detecting anomalous queries in transaction systems like e-commerce platform. The adaptive detection algorithm is necessary to find anomaly accesses when the environment changes continuously. To solve this problem, we used accuracy-based LCS (XCS), one of the primary model of adaptive machine learning method, for detecting malicious accesses in databases. In the problem of database intrusion detection which changes the detecting targets, we found and analyzed the patterns of rule generation to show systemically how the adaptive learning of XCS algorithm is working in practical usage.",

author = "Suin Seo and Cho, {Sung Bae}",

year = "2018",

month = jul,

day = "6",

doi = "10.1145/3205651.3208315",

language = "English",

series = "GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion",

publisher = "Association for Computing Machinery, Inc",

pages = "1442--1448",

booktitle = "GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion",

note = "2018 Genetic and Evolutionary Computation Conference, GECCO 2018 ; Conference date: 15-07-2018 Through 19-07-2018",

}

Seo, S & Cho, SB 2018, Applying accuracy-based LCS to detecting anomalous database access. in GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, Inc, pp. 1442-1448, 2018 Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, 18/7/15. https://doi.org/10.1145/3205651.3208315

Applying accuracy-based LCS to detecting anomalous database access. / Seo, Suin; Cho, Sung Bae.

GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2018. p. 1442-1448 (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion).

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

TY - GEN

T1 - Applying accuracy-based LCS to detecting anomalous database access

AU - Seo, Suin

AU - Cho, Sung Bae

PY - 2018/7/6

Y1 - 2018/7/6

N2 - Database intrusion detection (DB-IDS) is the problem of detecting anomalous queries in transaction systems like e-commerce platform. The adaptive detection algorithm is necessary to find anomaly accesses when the environment changes continuously. To solve this problem, we used accuracy-based LCS (XCS), one of the primary model of adaptive machine learning method, for detecting malicious accesses in databases. In the problem of database intrusion detection which changes the detecting targets, we found and analyzed the patterns of rule generation to show systemically how the adaptive learning of XCS algorithm is working in practical usage.

AB - Database intrusion detection (DB-IDS) is the problem of detecting anomalous queries in transaction systems like e-commerce platform. The adaptive detection algorithm is necessary to find anomaly accesses when the environment changes continuously. To solve this problem, we used accuracy-based LCS (XCS), one of the primary model of adaptive machine learning method, for detecting malicious accesses in databases. In the problem of database intrusion detection which changes the detecting targets, we found and analyzed the patterns of rule generation to show systemically how the adaptive learning of XCS algorithm is working in practical usage.

UR - http://www.scopus.com/inward/record.url?scp=85051559886&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051559886&partnerID=8YFLogxK

U2 - 10.1145/3205651.3208315

DO - 10.1145/3205651.3208315

M3 - Conference contribution

AN - SCOPUS:85051559886

T3 - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

SP - 1442

EP - 1448

BT - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

PB - Association for Computing Machinery, Inc

T2 - 2018 Genetic and Evolutionary Computation Conference, GECCO 2018

Y2 - 15 July 2018 through 19 July 2018

ER -

Seo S, Cho SB. Applying accuracy-based LCS to detecting anomalous database access. In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc. 2018. p. 1442-1448. (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion). https://doi.org/10.1145/3205651.3208315