Iterative Learning-Based Intrusion Detection System for Performance Enhancement in Imbalanced Data Environments

Yu-Ran Jeon, Il-Gu Lee, Il-Gu Lee

2025

Abstract

To defend against advanced cyberattacks, various anomaly detection methods have been developed, including signature-based, machine learning (ML)-based, and tool-based approaches across multiple fields. The ML-based anomaly detection method analyzes the patterns of the input data and identifies malicious behavior using classifiers. However, the ML-based anomaly detection method faces the challenge of accurately distinguishing malicious behavior from benign behavior, and its performance is reduced in real-world environments because of the discrepancies between training and deployment environments. In this study, cybersecurity challenges were analyzed, focusing on intrusion detection systems (IDS) and the influence of ML performance degradation in imbalanced data environments. To counteract this performance degradation, an optimal iterative learning-based IDS is proposed that improves efficiency by approximately 24% compared to a conventional model.

Paper Citation

in Harvard Style

Jeon Y. and Lee I. (2025). Iterative Learning-Based Intrusion Detection System for Performance Enhancement in Imbalanced Data Environments. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 318-324. DOI: 10.5220/0013253400003899

in Bibtex Style

@conference{icissp25,
author={Yu-Ran Jeon and Il-Gu Lee},
title={Iterative Learning-Based Intrusion Detection System for Performance Enhancement in Imbalanced Data Environments},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2025},
pages={318-324},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013253400003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - Iterative Learning-Based Intrusion Detection System for Performance Enhancement in Imbalanced Data Environments
SN - 978-989-758-735-1
AU - Jeon Y.
AU - Lee I.
PY - 2025
SP - 318
EP - 324
DO - 10.5220/0013253400003899
PB - SciTePress