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Machine Learning Approach for Cyberattack Detection and Prevention on IoT Networks

by Janet M. Maluki, Jimmy K.N. Macharia, Dalton Ndirangu Kaimuru
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 77
Year of Publication: 2025
Authors: Janet M. Maluki, Jimmy K.N. Macharia, Dalton Ndirangu Kaimuru
10.5120/ijca2025924613

Janet M. Maluki, Jimmy K.N. Macharia, Dalton Ndirangu Kaimuru . Machine Learning Approach for Cyberattack Detection and Prevention on IoT Networks. International Journal of Computer Applications. 186, 77 ( Apr 2025), 17-26. DOI=10.5120/ijca2025924613

@article{ 10.5120/ijca2025924613,
author = { Janet M. Maluki, Jimmy K.N. Macharia, Dalton Ndirangu Kaimuru },
title = { Machine Learning Approach for Cyberattack Detection and Prevention on IoT Networks },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2025 },
volume = { 186 },
number = { 77 },
month = { Apr },
year = { 2025 },
issn = { 0975-8887 },
pages = { 17-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number77/machine-learning-approach-for-cyberattack-detection-and-prevention-on-iot-networks/ },
doi = { 10.5120/ijca2025924613 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-04-05T01:33:44.427100+05:30
%A Janet M. Maluki
%A Jimmy K.N. Macharia
%A Dalton Ndirangu Kaimuru
%T Machine Learning Approach for Cyberattack Detection and Prevention on IoT Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 77
%P 17-26
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A key part of securing IoT networks is detecting intrusions and stopping potential attacks before they cause harm. To achieve this, various security measures have been implemented, including firewalls, intrusion detection systems, antivirus software, and organizational security policies. This study adopts a systematic approach to detecting and preventing cyberattacks in IoT networks. It examines prior research, evaluates existing intrusion detection techniques, and applies these insights to develop a more effective and adaptable detection framework. This study examines intrusion detection techniques that incorporate machine learning and statistical methods. Building on a thorough analysis of existing intrusion detection systems, it introduces a novel model that enhances multiple cyberattack detection and prevention in IoT networks. The experimental results highlight the model's strong performance, achieving an impressive 98% accuracy. It also maintains a weighted average recall of 97%, precision of 96%, and an F1-score of 96% across various attack categories, demonstrating its reliability in detecting multiple cyberattacks.

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Index Terms

Computer Science
Information Sciences
IoT
Security
Cyberattack
Detection
Machine Learning
Algorithms

Keywords

Intrusion attacks statistics models anomalous classification clustering detection framework Internet of Things

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