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.
|Title of host publication||Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings|
|Editors||Hector Quintian, Emilio Corchado, Francisco Javier [surname]Martinez de Pison, Ruben Urraca|
|Number of pages||11|
|Publication status||Published - 2017|
|Event||12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017 - La Rioja, Spain|
Duration: 2017 Jun 21 → 2017 Jun 23
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017|
|Period||17/6/21 → 17/6/23|
Bibliographical notePublisher Copyright:
© Springer International Publishing AG 2017.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)