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

Charissa Ann Ronao, Sung Bae Cho

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

10 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 languageEnglish
Title of host publicationArtificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Proceedings
EditorsRyszard Tadeusiewicz, Lotfi A. Zadeh, Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Jacek M. Zurada
PublisherSpringer Verlag
Pages36-48
Number of pages13
ISBN (Electronic)9783319193687
DOIs
Publication statusPublished - 2015 Jan 1
Event14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015 - Zakopane, Poland
Duration: 2015 Jun 142015 Jun 18

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume9120
ISSN (Print)0302-9743

Other

Other14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015
CountryPoland
CityZakopane
Period15/6/1415/6/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • 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