ItemConference Paper

Efficient Intrusion Detection through Class Balancing and Feature Selection: A Case Study with SVM

Title
Efficient Intrusion Detection through Class Balancing and Feature Selection: A Case Study with SVM
Creator
Fernandes, Melsa Magdalene; Pereira, Sharlene Anna; Anita, EA Mary; Sowmya, T.
Description
Intrusion Detection Systems are of paramount importance in network security. However, in real-world scenarios, they always suffer from the challenge of class imbalance, which is dominated by normal traffic. This paper presents a novel approach to enhancing the performance of IDS by proposing a hybrid of the Random Under sampling technique with the univariate feature selection technique, SelectKBest, for handling both problems of class imbalance and high dimensionality. This model was hence tried on the Bot-IoT dataset, which is a real-world IoT network traffic representation. The SVM classifier, which has been trained with the resampled and feature-selected data, showcased 95% balanced accuracy for both normal and malicious traffic detection. The combination of RUS and SelectKBest, apart from reducing overfitting, ensured the retention of the most relevant features and thereby made the IDS model robust. It can practically enhance the performance of IDS in an imbalanced and high-dimensional dataset by providing a balanced, efficient, and precise detecting mechanism. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1296 LNNS;pp.1-9
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Bot-IoT dataset; Class imbalance; Feature selection; Intrusion Detection Systems (IDS); Random under sampling (RUS); SelectKBest; Support Vector Machine (SVM)
Coverage
Fernandes M.M., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Karnataka, Bangalore, India; Pereira S.A., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Karnataka, Bangalore, India; Anita E.A.M., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Karnataka, Bangalore, India; Sowmya T., Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Karnataka, Manipal, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981963313-5;
Format
online
Language
English
Type
Conference paper

Citation

Fernandes, Melsa Magdalene; Pereira, Sharlene Anna; Anita, EA Mary; Sowmya, T., “Efficient Intrusion Detection through Class Balancing and Feature Selection: A Case Study with SVM,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25511.

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