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Advances in Intrusion Detection Systems: Integrating Machine Learning, Deep Learning, IoT, and Federated Learning

by Ziadul Amin Chowdhury, M.M. Rahman, Tanvir Azhar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 58
Year of Publication: 2024
Authors: Ziadul Amin Chowdhury, M.M. Rahman, Tanvir Azhar
10.5120/ijca2024924340

Ziadul Amin Chowdhury, M.M. Rahman, Tanvir Azhar . Advances in Intrusion Detection Systems: Integrating Machine Learning, Deep Learning, IoT, and Federated Learning. International Journal of Computer Applications. 186, 58 ( Dec 2024), 21-28. DOI=10.5120/ijca2024924340

@article{ 10.5120/ijca2024924340,
author = { Ziadul Amin Chowdhury, M.M. Rahman, Tanvir Azhar },
title = { Advances in Intrusion Detection Systems: Integrating Machine Learning, Deep Learning, IoT, and Federated Learning },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2024 },
volume = { 186 },
number = { 58 },
month = { Dec },
year = { 2024 },
issn = { 0975-8887 },
pages = { 21-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number58/advances-in-intrusion-detection-systems-integrating-machine-learning-deep-learning-iot-and-federated-learning/ },
doi = { 10.5120/ijca2024924340 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-12-27T02:46:14.171282+05:30
%A Ziadul Amin Chowdhury
%A M.M. Rahman
%A Tanvir Azhar
%T Advances in Intrusion Detection Systems: Integrating Machine Learning, Deep Learning, IoT, and Federated Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 58
%P 21-28
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The integration of Machine Learning (ML) and Deep Learning (DL) techniques has ushered in a new era of Intrusion Detection Systems (IDS). These advanced approaches significantly enhance detection accuracy, enabling the identification of novel cyber threats and processing massive datasets to ensure robust and reliable network security. The synergy between IoT devices and Federated Learning empowers IDSs to handle distributed data sources and secure edge environments effectively. By leveraging diverse datasets, including network traffic, system logs, and user behavior, IDSs can construct comprehensive threat models and improve their overall effectiveness. This paper investigates cutting-edge methodologies and models based on ML, DL, IoT, and Federated Learning. The challenges associated with deploying DL and ML in IDS have been discussed, and potential avenues for future research have been proposed. This survey aims to guide researchers in adopting contemporary network security and intrusion detection techniques.

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Index Terms

Computer Science
Information Sciences
Intrusion Detection Techniques – IDS; IoT – Internet of Things
XAI – Explainable Artificial Intelligence; Federated Learning – FL

Keywords

IDS Deep Learning Machine Learning Federated Learning Cyber security IoT

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