research-article

A Novel Hybrid Feature Selection with Cascaded LSTM: : Enhancing Security in IoT Networks

Authors:

Karthic Sundaram,

Yuvaraj Natarajan,

Anitha Perumalsamy,

Ahmed Abdi Yusuf Ali Academic Editor:

Sandhya AnejaAuthors Info & Claims

Wireless Communications and Mobile Computing, Volume 2024

https://doi.org/10.1155/2024/5522431

Published: 13 March 2024 Publication History

Abstract

The rapid growth of the Internet of Things (IoT) has created a situation where a huge amount of sensitive data is constantly being created and sent through many devices, making data security a top priority. In the complex network of IoT, detecting intrusions becomes a key part of strengthening security. Since IoT environments can be easily affected by a wide range of cyber threats, intrusion detection systems (IDS) are crucial for quickly finding and dealing with potential intrusions as they happen. IDS datasets can have a wide range of features, from just a few to several hundreds or even thousands. Managing such large datasets is a big challenge, requiring a lot of computer power and leading to long processing times. To build an efficient IDS, this article introduces a combined feature selection strategy using recursive feature elimination and information gain. Then, a cascaded long–short-term memory is used to improve attack classifications. This method achieved an accuracy of 98.96% and 99.30% on the NSL-KDD and UNSW-NB15 datasets, respectively, for performing binary classification. This research provides a practical strategy for improving the effectiveness and accuracy of intrusion detection in IoT networks.

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Cited By

Nandhini SRajeswari AShanker N(2024)Cyber attack detection in IOT-WSN devices with threat intelligence using hidden and connected layer based architecturesJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00722-913:1Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1186/s13677-024-00722-9

Index Terms

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
2. Security and privacy

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Wireless Communications & Mobile Computing

Wireless Communications & Mobile Computing Volume 2024, Issue

2024

228 pages

ISSN:1530-8669

Issue’s Table of Contents

Copyright © 2024 Karthic Sundaram et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Publisher

John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 13 March 2024

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Cited By

Nandhini SRajeswari AShanker N(2024)Cyber attack detection in IOT-WSN devices with threat intelligence using hidden and connected layer based architecturesJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00722-913:1Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1186/s13677-024-00722-9

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