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Deep Learning Models for Cybersecurity in IoT Networks

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Meriem Naji
Hicham Zougagh
Published in
Business Intelligence
Publisher
Springer Nature Switzerland

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Abstract

The chapter delves into the critical role of cybersecurity in the Internet of Things (IoT) and the challenges posed by the diverse and heterogeneous nature of IoT devices. It introduces deep learning models, such as Long Short-Term Memory (LSTM), Autoencoders (AE), and Multilayer Perceptron (MLP), as advanced solutions for intrusion detection. The study utilizes the NSL-KDD dataset to evaluate the performance of these models in detecting various types of network attacks. The chapter highlights the effectiveness of deep learning techniques in outperforming traditional methods, providing a detailed analysis of the models' accuracy, precision, recall, and F1 scores. It also discusses potential future directions, including the exploration of other deep learning methods, real-time detection models, and the optimization of hyperparameters.
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previous chapter A Systematic Analysis for Machine Learning Based Cow Price Prediction
next chapter Diabetes Prediction by Machine Learning Algorithms and Risks Factors
Title
Deep Learning Models for Cybersecurity in IoT Networks
Authors
Meriem Naji
Hicham Zougagh
Copyright Year
2023
Book
Business Intelligence

Print ISBN: 978-3-031-37871-3

Electronic ISBN: 978-3-031-37872-0

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-37872-0_3
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