Intrusion Detection Schemes Based on Synthetic Minority Oversampling Technique and Machine Learning Models-Reference-Cited by-同舟云学术

Intrusion Detection Schemes Based on Synthetic Minority Oversampling Technique and Machine Learning Models

Published:2024-05-22 Issue: Volume: Page:1-8
ISSN:
Container-title:2024 IEEE 27th International Symposium on Real-Time Distributed Computing (ISORC)
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Author:

Ali Ali Hussein¹,Charfeddine Maha²,Ammar Boudour²,Hamed Bassem Ben³

Affiliation:

1. University of Sfax, National School of Electronics and Telecommunications of Sfax,REGIM-Lab: REsearch Groups in Intelligent Machines,Tunisia

2. University of Sfax, National Engineering School of Sfax (ENIS),REGIM-Lab: REsearch Groups in Intelligent Machines,Sfax,Tunisia,3038

3. University of Sfax, National School of Electronics and Telecommunications of Sfax,Laboratory of Signals, systeMs, aRtificial Intelligence and neTworkS (SM@RTS),Tunisia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10551300/10551323/10551335.pdf?arnumber=10551335

Reference31 articles.

1. DL‐IDS: a deep learning–based intrusion detection framework for securing IoT

2. Deep Learning Based Network Intrusion Detection System for Resource-Constrained Environments

3. Feature extraction for machine learning-based intrusion detection in IoT networks

4. Intrusion detection system in distributed cloud computing: Hybrid clustering and classification methods

5. Analysis of Intrusion Detection Systems in UNSW-NB15 and NSL-KDD Datasets with Machine Learning Algorithms

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