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

Pimsarn, Chutipon, Boongoen, Tossapon, Iam-On, Natthakan, Naik, Nitin and Yang, Longzhi (2022) Strengthening intrusion detection system for adversarial attacks: improved handling of imbalance classification problem. Complex & Intelligent Systems. ISSN 2199-4536 (In Press)

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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.

Item Type: Article
Additional Information: 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).
Uncontrolled Keywords: Adversarial attack, Data clustering, Imbalance classification, Intrusion detection system, Machine learning
Subjects: G100 Mathematics
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 03 Aug 2022 08:10
Last Modified: 03 Aug 2022 08:15

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