Strengthening intrusion detection system for adversarial attacks: improved handling of imbalance classification problem

Chutipon Pimsarn, Tossapon Boongoen*, Natthakan Iam-On, Nitin Naik, Longzhi Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Most defence mechanisms such as a network-based intrusion detection system (NIDS) are often sub-optimal for the detection of an unseen malicious pattern. In response, a number of studies attempt to empower a machine-learning-based NIDS to improve the ability to recognize adversarial attacks. Along this line of research, the present work focuses on non-payload connections at the TCP stack level, which is generalized and applicable to different network applications. As a compliment to the recently published investigation that searches for the most informative feature space for classifying obfuscated connections, the problem of class imbalance is examined herein. In particular, a multiple-clustering-based undersampling framework is proposed to determine the set of cluster centroids that best represent the majority class, whose size is reduced to be on par with that of the minority. Initially, a pool of centroids is created using the concept of ensemble clustering that aims to obtain a collection of accurate and diverse clusterings. From that, the final set of representatives is selected from this pool. Three different objective functions are formed for this optimization driven process, thus leading to three variants of FF-Majority, FF-Minority and FF-Overall. Based on the thorough evaluation of a published dataset, four classification models and different settings, these new methods often exhibit better predictive performance than its baseline, the single-clustering undersampling counterpart and state-of-the-art techniques. Parameter analysis and implication for analyzing an extreme case are also provided as a guideline for future applications.
Original languageEnglish
Pages (from-to)4863-4880
JournalComplex & Intelligent Systems
Issue number6
Early online date25 Apr 2022
Publication statusPublished - Dec 2022

Bibliographical note

© 2022, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Funding Information:
This research work is partly supported by Mae Fah Luang University, Newton IAPP 2017 (Royal Academy of Engineering and Thailand Research Fund), and Newton Institutional Links 2020-21 project (British Council and National Research Council of Thailand).


  • Intrusion Detection System
  • Machine Learning
  • Adversarial Attack
  • Imbalance Classification
  • Data Clustering


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