Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning-Reference-Cited by-同舟云学术

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Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning

Published:2024-08 Issue:4 Volume:29 Page:948-958
ISSN:1007-0214
Container-title:Tsinghua Science and Technology
language:
Short-container-title:Tsinghua Sci. Technol.

Author:

Eljialy A. E. M.¹,Uddin Mohammed Yousuf¹,Ahmad Sultan²

Affiliation:

1. College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University,Department of Information Systems,Alkharj,Saudi Arabia

2. Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University,Alkharj,Saudi Arabia

Publisher

Tsinghua University Press

Link

http://xplorestaging.ieee.org/ielx7/5971803/10431706/10431760.pdf?arnumber=10431760

Reference29 articles.

1. Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

2. A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems

3. Analysis of Intrusion Detection Approaches for Network Traffic Anomalies with Comparative Analysis on Botnets (2008–2020)

4. A novel intrusion detection system for wireless mesh network with hybrid feature selection technique based on GA and MI

5. IoT malicious traffic identification using wrapper-based feature selection mechanisms

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1. Res2Net-ERNN: deep learning based cyberattack classification in software defined network;Cluster Computing;2024-06-17

2. Internet-of-Things-Enabled Smart Agriculture: Security Enhancement Approaches;2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET);2024-05-16

3. Internet of Things Authentication Protocols: Comparative Study;Computers, Materials & Continua;2024

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