A hybrid system of deep learning and learning classifier system for database intrusion detection

Seok Jun Bu, Sung Bae Cho

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

9 Citations (Scopus)

Abstract

Nowadays, as most of the companies and organizations rely on the database to safeguard sensitive data, it is required to guarantee the strong protection of the data. Intrusion detection system (IDS) can be an important component of the strong security framework, and the machine learning approach with adaptation capability has a great advantage for this system. In this paper, we propose a hybrid system of convolutional neural network (CNN) and learning classifier system (LCS) for IDS, called Convolutional Neural-Learning Classifier System (CN-LCS). CNN, one of the deep learning methods for image and pattern classification, classifies the queries by modeling normal behaviors of database. LCS, one of the adapted heuristic search algorithms based on genetic algorithm, discovers new rules to detect abnormal behaviors to supplement the CNN. Experiments with TPC-E benchmark database show that CN-LCS yields the best classification accuracy compared to other state-of-the-art machine learning algorithms. Additional analysis by t-SNE algorithm reveals the common patterns among highly misclassified queries.

Original languageEnglish
Title of host publicationHybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings
EditorsHector Quintian, Emilio Corchado, Francisco Javier [surname]Martinez de Pison, Ruben Urraca
PublisherSpringer Verlag
Pages615-625
Number of pages11
ISBN (Print)9783319596495
DOIs
Publication statusPublished - 2017 Jan 1
Event12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017 - La Rioja, Spain
Duration: 2017 Jun 212017 Jun 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10334 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017
CountrySpain
CityLa Rioja
Period17/6/2117/6/23

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Bu, S. J., & Cho, S. B. (2017). A hybrid system of deep learning and learning classifier system for database intrusion detection. In H. Quintian, E. Corchado, F. J. [surname]Martinez de Pison, & R. Urraca (Eds.), Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings (pp. 615-625). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10334 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59650-1_52