An intrusion detection system in the Internet of Things with deep learning and an improved arithmetic optimization algorithm (AOA) and sine cosine algorithm (SCA)

The Internet of Things (IoT) is vulnerable to cyber-attacks due to limited security mechanisms and resources constraints. Traditional intrusion detection systems (IDS) deal with imbalanced datasets, high-dimensional network traffic, and the inability to detect new attacks. This research proposes an...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 38156 - 25
Hlavní autor: Ghadami, Raheleh
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 31.10.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/166/987
639/705/117
Algorithms
Datasets
Deep learning
Denial of service attacks
Feature selection
Game theory
Generative adversarial network (GAN)
Humanities and Social Sciences
Internet of Things
Internet of Things (IoT)
Intrusion detection system (IDS)
Long short-term memory
multidisciplinary
Neural networks
Parallel convolutional neural network (PCNN)
Science
Science (multidisciplinary)
Sensors
Spatial discrimination learning
Internet of Things (IoT)
Feature selection
Intrusion detection system (IDS)
Generative adversarial network (GAN)
Game theory
Parallel convolutional neural network (PCNN)
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí:The Internet of Things (IoT) is vulnerable to cyber-attacks due to limited security mechanisms and resources constraints. Traditional intrusion detection systems (IDS) deal with imbalanced datasets, high-dimensional network traffic, and the inability to detect new attacks. This research proposes an advanced IDS framework that utilizes game-theory-based Generative Adversarial Networks (GAN) for dataset balancing, a hybrid Arithmetic Optimization Algorithm (AOA), and a Sine Cosine Algorithm (SCA) for feature selection, and a Parallel Convolutional Neural Network (PCNN) combined with Long Short-Term Memory (LSTM) layer for accurate attack detection. The suggested ASPCNNLSTM model achieves a precision of 99.86% on the NSL-KDD dataset and an attack detection accuracy of 98.67% on the UNSW-NB15 dataset, significantly outperforming traditional CNN, LSTM, and feature selection methods. This strategy improves IDS capability by selecting the best features relevant to traffic features and improving spatial and temporal feature learning, making it robust against complex and unknown cyber threats.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-22074-3