Toward an online network intrusion detection system based on ensemble learning

Ying Feng Hsu, Zhen Yu He, Yuya Tarutani, Morito Matsuoka

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

27 Citations (Scopus)


With information technology growing and rapidly increasing, ubiquitous networking technology generates a massive amount of data and is integrated into our daily life. Network intrusion detection systems (NIDS) are essential for organizations to ensure the safety and security of their communication and information. In general, there are two types of NIDS: signature-based (SNIDS) and anomaly-based (ANDIS). Most modern NIDS solutions are signature-based techniques, which require a routine signature update and cannot detect unknown types of attacks. However, ANDIS has been extensively studied and is considered a better alternative to NIDS. In this paper, we present a stacked ensemble learning based ANIDS that consists of autoencoder (AE), support vector machine (SVM), and random forest (RF) models. To show the overall applicability of our approach, we demonstrate our work through two well-known NIDS benchmark datasets: NSL-KDD and UNSW-NB15 and a real campus network log, which includes about 300 million daily records. We compare our method to three different machine learning classical models and two other reported study results. Our test result implies that our proposed method can also limit both false positive and false negative predictions.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Cloud Computing, CLOUD 2019 - Part of the 2019 IEEE World Congress on Services
EditorsElisa Bertino, Carl K. Chang, Peter Chen, Ernesto Damiani, Michael Goul, Katsunori Oyama
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781728127057
Publication statusPublished - Jul 2019
Event12th IEEE International Conference on Cloud Computing, CLOUD 2019 - Milan, Italy
Duration: Jul 8 2019Jul 13 2019

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190


Conference12th IEEE International Conference on Cloud Computing, CLOUD 2019


  • Autoencoder
  • Deep learning
  • Ensemble learning
  • NIDS
  • Network intrusion detection system
  • Random forest
  • Support vector machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Software


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