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A Hybrid System of Deep Learning and Learning Classifier System for Database Intrusion Detection

  • Seok-Jun Bu
  • Sung-Bae ChoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)

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.

Keywords

Feature Selection Intrusion Detection Machine Learning Algorithm Intrusion Detection System Convolutional Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

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

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceYonsei UniversitySeoulSouth Korea

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