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Studying Machine Learning Techniques for Intrusion Detection Systems

  • Quang-Vinh DangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11814)

Abstract

Intrusion detection systems (IDSs) have been studied widely in the computer security community for a long time. The recent development of machine learning techniques has boosted the performance of the intrusion detection systems significantly. However, most modern machine learning and deep learning algorithms are exhaustive of labeled data that requires a lot of time and effort to collect. Furthermore, it might be late until all the data is collected to train the model.

In this study, we first perform a comprehensive survey of existing studies on using machine learning for IDSs. Hence we present two approaches to detect the network attacks. We present that by using a tree-based ensemble learning with feature engineering we can outperform state-of-the-art results in the field. We also present a new approach in selecting training data for IDSs hence by using a small subset of training data combined with some weak classification algorithms we can improve the performance of the detector while maintaining the low running cost.

Keywords

Intrusion Detection System Machine learning Classification 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Data Innovation LabIndustrial University of Ho Chi Minh CityHo Chi Minh CityVietnam

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