Random forests with weighted voting for anomalous query access detection in relational databases

Charissa Ann Ronao; Sung Bae Cho

doi:10.1007/978-3-319-19369-4_4

Random forests with weighted voting for anomalous query access detection in relational databases

Charissa Ann Ronao, Sung Bae Cho

College of Computing

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

14 Citations (Scopus)

Abstract

Data has become more and more important to individuals, organizations, and companies, and therefore, safeguarding these sensitive data in relational databases has become a critical issue. However, despite traditional security mechanisms, attacks directed to databases still occur. Thus, an intrusion detection system (IDS) specifically for the database that can provide protection from all possible malicious users is necessary. In this paper, we present a random forests (RF) method with weighted voting for the task of anomaly detection. RF is a graph-based technique suitable for modeling SQL queries, and weighted voting enhances its capabilities by balancing the voting impact of each tree. Experiments show that RF with weighted voting exhibits a more superior performance consistency, as well as better error rates with increasing number of trees, compared to conventional RF. Moreover, it outperforms all other stateof- The-art data mining algorithms in terms of false positive rate (0.076) and false negative rate (0.0028).

Original language	English
Title of host publication	Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Proceedings
Editors	Ryszard Tadeusiewicz, Lotfi A. Zadeh, Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Jacek M. Zurada
Publisher	Springer Verlag
Pages	36-48
Number of pages	13
ISBN (Electronic)	9783319193687
DOIs	https://doi.org/10.1007/978-3-319-19369-4_4
Publication status	Published - 2015
Event	14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015 - Zakopane, Poland Duration: 2015 Jun 14 → 2015 Jun 18

Publication series

Name	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	9120
ISSN (Print)	0302-9743

Other

Other	14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015
Country/Territory	Poland
City	Zakopane
Period	15/6/14 → 15/6/18

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-19369-4_4

Cite this

Ronao, C. A., & Cho, S. B. (2015). Random forests with weighted voting for anomalous query access detection in relational databases. In R. Tadeusiewicz, L. A. Zadeh, L. Rutkowski, M. Korytkowski, R. Scherer, & J. M. Zurada (Eds.), Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Proceedings (pp. 36-48). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 9120). Springer Verlag. https://doi.org/10.1007/978-3-319-19369-4_4

Ronao, Charissa Ann ; Cho, Sung Bae. / Random forests with weighted voting for anomalous query access detection in relational databases. Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Proceedings. editor / Ryszard Tadeusiewicz ; Lotfi A. Zadeh ; Leszek Rutkowski ; Marcin Korytkowski ; Rafal Scherer ; Jacek M. Zurada. Springer Verlag, 2015. pp. 36-48 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)).

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title = "Random forests with weighted voting for anomalous query access detection in relational databases",

abstract = "Data has become more and more important to individuals, organizations, and companies, and therefore, safeguarding these sensitive data in relational databases has become a critical issue. However, despite traditional security mechanisms, attacks directed to databases still occur. Thus, an intrusion detection system (IDS) specifically for the database that can provide protection from all possible malicious users is necessary. In this paper, we present a random forests (RF) method with weighted voting for the task of anomaly detection. RF is a graph-based technique suitable for modeling SQL queries, and weighted voting enhances its capabilities by balancing the voting impact of each tree. Experiments show that RF with weighted voting exhibits a more superior performance consistency, as well as better error rates with increasing number of trees, compared to conventional RF. Moreover, it outperforms all other stateof- The-art data mining algorithms in terms of false positive rate (0.076) and false negative rate (0.0028).",

author = "Ronao, {Charissa Ann} and Cho, {Sung Bae}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015 ; Conference date: 14-06-2015 Through 18-06-2015",

year = "2015",

doi = "10.1007/978-3-319-19369-4_4",

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Ronao, CA & Cho, SB 2015, Random forests with weighted voting for anomalous query access detection in relational databases. in R Tadeusiewicz, LA Zadeh, L Rutkowski, M Korytkowski, R Scherer & JM Zurada (eds), Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Proceedings. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 9120, Springer Verlag, pp. 36-48, 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015, Zakopane, Poland, 15/6/14. https://doi.org/10.1007/978-3-319-19369-4_4

Random forests with weighted voting for anomalous query access detection in relational databases. / Ronao, Charissa Ann; Cho, Sung Bae.
Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Proceedings. ed. / Ryszard Tadeusiewicz; Lotfi A. Zadeh; Leszek Rutkowski; Marcin Korytkowski; Rafal Scherer; Jacek M. Zurada. Springer Verlag, 2015. p. 36-48 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 9120).

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

TY - GEN

T1 - Random forests with weighted voting for anomalous query access detection in relational databases

AU - Ronao, Charissa Ann

AU - Cho, Sung Bae

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Data has become more and more important to individuals, organizations, and companies, and therefore, safeguarding these sensitive data in relational databases has become a critical issue. However, despite traditional security mechanisms, attacks directed to databases still occur. Thus, an intrusion detection system (IDS) specifically for the database that can provide protection from all possible malicious users is necessary. In this paper, we present a random forests (RF) method with weighted voting for the task of anomaly detection. RF is a graph-based technique suitable for modeling SQL queries, and weighted voting enhances its capabilities by balancing the voting impact of each tree. Experiments show that RF with weighted voting exhibits a more superior performance consistency, as well as better error rates with increasing number of trees, compared to conventional RF. Moreover, it outperforms all other stateof- The-art data mining algorithms in terms of false positive rate (0.076) and false negative rate (0.0028).

AB - Data has become more and more important to individuals, organizations, and companies, and therefore, safeguarding these sensitive data in relational databases has become a critical issue. However, despite traditional security mechanisms, attacks directed to databases still occur. Thus, an intrusion detection system (IDS) specifically for the database that can provide protection from all possible malicious users is necessary. In this paper, we present a random forests (RF) method with weighted voting for the task of anomaly detection. RF is a graph-based technique suitable for modeling SQL queries, and weighted voting enhances its capabilities by balancing the voting impact of each tree. Experiments show that RF with weighted voting exhibits a more superior performance consistency, as well as better error rates with increasing number of trees, compared to conventional RF. Moreover, it outperforms all other stateof- The-art data mining algorithms in terms of false positive rate (0.076) and false negative rate (0.0028).

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M3 - Conference contribution

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T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

SP - 36

EP - 48

BT - Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Proceedings

A2 - Tadeusiewicz, Ryszard

A2 - Zadeh, Lotfi A.

A2 - Rutkowski, Leszek

A2 - Korytkowski, Marcin

A2 - Scherer, Rafal

A2 - Zurada, Jacek M.

PB - Springer Verlag

T2 - 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015

Y2 - 14 June 2015 through 18 June 2015

ER -

Ronao CA, Cho SB. Random forests with weighted voting for anomalous query access detection in relational databases. In Tadeusiewicz R, Zadeh LA, Rutkowski L, Korytkowski M, Scherer R, Zurada JM, editors, Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Proceedings. Springer Verlag. 2015. p. 36-48. (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)). doi: 10.1007/978-3-319-19369-4_4

Random forests with weighted voting for anomalous query access detection in relational databases

Abstract

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