Skip to main content
Top
Published in:

27-09-2024

An ensemble learning approach for intrusion detection in IoT-based smart cities

Authors: G. Indra, E. Nirmala, G. Nirmala, P. Gururama Senthilvel

Published in: Peer-to-Peer Networking and Applications | Issue 6/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The article introduces an innovative ensemble learning approach for intrusion detection in IoT-based smart cities, addressing the urgent need for urbanization and the subsequent challenges in maintaining city environments. The rapid population growth and increasing population density in cities have led to the concept of smart cities, which rely heavily on the Internet of Things (IoT) for managing various processes. However, IoT networks are vulnerable to cyber attacks due to their transparent nature and the vast amount of data they handle. The proposed Ensemble Gradient Random Forest-based Leopard Seal Search (EGR-LSS) algorithm aims to enhance intrusion detection systems' accuracy and efficiency. The article details the architecture of the EGR-LSS method, which includes terminal, fog, and cloud layers, and explains how data preprocessing and anomaly detection are performed using Gradient Boosting and Random Forest techniques. The Leopard Seal Optimization (LSO) algorithm is employed for hyperparameter optimization, ensuring the model's high performance. The research also highlights the advantages of the EGR-LSS approach over existing methods, demonstrating its superior accuracy and efficiency in detecting various types of cyber threats. The article concludes with a discussion of the proposed model's performance and future research directions, emphasizing the need for real-world implementation and the development of advanced techniques to handle evolving threats in smart cities.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Business + Economics & Engineering + Technology"

Online-Abonnement

Springer Professional "Business + Economics & Engineering + Technology" gives you access to:

  • more than 102.000 books
  • more than 537 journals

from the following subject areas:

  • Automotive
  • Construction + Real Estate
  • Business IT + Informatics
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Finance + Banking
  • Management + Leadership
  • Marketing + Sales
  • Mechanical Engineering + Materials
  • Insurance + Risk


Secure your knowledge advantage now!

Springer Professional "Engineering + Technology"

Online-Abonnement

Springer Professional "Engineering + Technology" gives you access to:

  • more than 67.000 books
  • more than 390 journals

from the following specialised fileds:

  • Automotive
  • Business IT + Informatics
  • Construction + Real Estate
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Mechanical Engineering + Materials





 

Secure your knowledge advantage now!

Springer Professional "Business + Economics"

Online-Abonnement

Springer Professional "Business + Economics" gives you access to:

  • more than 67.000 books
  • more than 340 journals

from the following specialised fileds:

  • Construction + Real Estate
  • Business IT + Informatics
  • Finance + Banking
  • Management + Leadership
  • Marketing + Sales
  • Insurance + Risk



Secure your knowledge advantage now!

Literature
This content is only visible if you are logged in and have the appropriate permissions.
Metadata
Title
An ensemble learning approach for intrusion detection in IoT-based smart cities
Authors
G. Indra
E. Nirmala
G. Nirmala
P. Gururama Senthilvel
Publication date
27-09-2024
Publisher
Springer US
Published in
Peer-to-Peer Networking and Applications / Issue 6/2024
Print ISSN: 1936-6442
Electronic ISSN: 1936-6450
DOI
https://doi.org/10.1007/s12083-024-01776-x

Premium Partner