Enhancing Network Intrusion Detection Using Effective Stacking of Ensemble Classifiers With Multi-Pronged Feature Selection Technique-Reference-Cited by-同舟云学术

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Enhancing Network Intrusion Detection Using Effective Stacking of Ensemble Classifiers With Multi-Pronged Feature Selection Technique

Published:2023-11-27 Issue: Volume:12 Page:1-6
ISSN:
Container-title:2023 2nd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)
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Author:

Rahman Saifur¹,Mursal Salim Nasar Faraj¹,Latif Muhammad Armghan²,Mushtaq Zohaib³,Irfan Muhammad¹,Waqar Ali⁴

Affiliation:

1. Najran University,College of Engineering,Department of Electrical Engineering,Najran,Saudi Arabia

2. Cleveland State University,Department of Computer and Information System,United States

3. University of Sargodha,College of Engineering and Technology,Department of Electrical Engineering,Sargodha,Pakistan

4. North China electric power University,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10395910/10396660/10396717.pdf?arnumber=10396717

Reference17 articles.

1. Enhanced Classification of Coffee Leaf Biotic Stress by Synergizing Feature Concatenation and Dimensionality Reduction

2. Improving Bearing Fault Identification by Using Novel Hybrid Involution-Convolution Feature Extraction With Adversarial Noise Injection in Conditional GANs

3. A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA

4. Deep Neural Network Based Real-Time Intrusion Detection System

5. Deep Learning Approach on Network Intrusion Detection System using NSL-KDD Dataset

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1. Securing Cloud-Encrypted Data: Detecting Ransomware-as-a-Service (RaaS) Attacks through Deep Learning Ensemble;Computers, Materials & Continua;2024

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