A Review of Imbalanced Datasets and Resampling Techniques in Network Intrusion Detection System-Reference-Cited by-同舟云学术

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A Review of Imbalanced Datasets and Resampling Techniques in Network Intrusion Detection System

Published:2023-11-17 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 8th International Conference on Information Technology and Digital Applications (ICITDA)
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Author:

Rajasa Mahesa Cadi¹,Rahma Fayruz²,Rachmadi Reza Fuad³,Pratomo Baskoro Adi⁴,Purnomo Mauridhi Hery³

Affiliation:

1. Universitas Islam Indonesia,Department of Informatics,Yogyakarta,Indonesia

2. Institut Teknologi Sepuluh Nopember,Department of Electrical Engineering,Surabaya,Indonesia

3. Institut Teknologi Sepuluh Nopember,Department of Computer Engineering,Surabaya,Indonesia

4. Institut Teknologi Sepuluh Nopember,Department of Informatics,Surabaya,Indonesia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10426788/10426842/10427217.pdf?arnumber=10427217

Reference62 articles.

1. LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system

2. Anomaly based network intrusion detection for IoT attacks using deep learning technique

3. Enhanced detection of imbalanced malicious network traffic with regularized Generative Adversarial Networks

4. An optimized ensemble prediction model using AutoML based on soft voting classifier for network intrusion detection

5. Assessing the Effectiveness of Oversampling and Undersampling Techniques for Intrusion Detection on an Imbalanced Dataset

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1. Resampling Effects on Imbalanced Data in Network Intrusion Classification;2024 International Electronics Symposium (IES);2024-08-06

2. Sentiment-based predictive models for online purchases in the era of marketing 5.0: a systematic review;Journal of Big Data;2024-08-05

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