Improving Performance of Intrusion Detection Using ALO Selected Features and GRU Network-Reference-Cited by-同舟云学术

Improving Performance of Intrusion Detection Using ALO Selected Features and GRU Network

Published:2023-10-21 Issue:6 Volume:4 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Sundaram Karthic^ORCID,Subramanian Suhana,Natarajan Yuvaraj,Thirumalaisamy Sumathi

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s42979-023-02311-0.pdf

Reference31 articles.

1. Heidari A, Jabraeil Jamali MA. Internet of Things intrusion detection systems: a comprehensive review and future directions. Cluster Comput. 2022. https://doi.org/10.1007/s10586-022-03776-z.

2. Disha RA, Waheed S. Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique. Cybersecurity. 2022;5:1–22.

3. Kumar P, et al. Toward design of an intelligent cyber attack detection system using hybrid feature reduced approach for IoT networks. Arab J Sci Eng. 2021;46:3749–78.

4. Gowdhaman V, Dhanapal R. An intrusion detection system for wireless sensor networks using deep neural network. Soft Comput. 2021;26:13059–67.

5. Naveed M, Arif F, Usman SM, Anwar A, Hadjouni M, Elmannai H, Hussain S, Ullah SS, Umar F. A deep learning-based framework for feature extraction and classification of intrusion detection in networks. Wirel Commun Mob Comput. 2022. https://doi.org/10.1155/2022/2215852. (Article ID 2215852).

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