Article: Consumer IoT device deployment optimisation through deep learning: a CNN-LSTM solution for traffic classification and service identification Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2024 Vol.45 No.2 pp.65 - 81 Abstract: The internet of things (IoT) has revolutionised our world, connecting devices and creating a more intelligent and interconnected environment. However, managing and utilising the vast amount of data generated by these devices is a major challenge. To address this, we propose a novel approach in this article that combines convolutional neural networks (CNNs) with long short-term memory (LSTM) networks to optimise IoT device deployment. The process involves data preparation, defining and training a deep learning model on preprocessed data, and using the trained model to categorise network traffic from IoT devices. Our experimental results demonstrate exceptional accuracy of over 99.99%. We evaluate the model's performance using classification metrics and compare it with commonly used traffic predictive models. Additionally, our approach provides valuable insights into the services offered by IoT devices by analysing their traffic patterns, distinguishing between monitoring, home automation, and appliance usage. Inderscience Publishers - linking academia, business and industry through research

Title: Consumer IoT device deployment optimisation through deep learning: a CNN-LSTM solution for traffic classification and service identification

Authors: Imane Chakour; Sajida Mhammedi; Cherki Daoui; Mohamed Baslam

Addresses: Faculty of Science and Technology, Sultan Moulay Slimane University, B.P. 523, Beni Mellal, Morocco ' National School of Applied Sciences, Sultan Moulay Slimane University, B.P. 523, Beni Mellal, Morocco ' Faculty of Science and Technology, Sultan Moulay Slimane University, B.P. 523, Beni Mellal, Morocco ' Faculty of Science and Technology, Sultan Moulay Slimane University, B.P. 523, Beni Mellal, Morocco

Abstract: The internet of things (IoT) has revolutionised our world, connecting devices and creating a more intelligent and interconnected environment. However, managing and utilising the vast amount of data generated by these devices is a major challenge. To address this, we propose a novel approach in this article that combines convolutional neural networks (CNNs) with long short-term memory (LSTM) networks to optimise IoT device deployment. The process involves data preparation, defining and training a deep learning model on preprocessed data, and using the trained model to categorise network traffic from IoT devices. Our experimental results demonstrate exceptional accuracy of over 99.99%. We evaluate the model's performance using classification metrics and compare it with commonly used traffic predictive models. Additionally, our approach provides valuable insights into the services offered by IoT devices by analysing their traffic patterns, distinguishing between monitoring, home automation, and appliance usage.

Keywords: internet of things; IoT; consumer IoT devices; convolutional neural network; CNN; long short-term memory; LSTM; IoT traffic classification; traffic analysis.

DOI: 10.1504/IJAHUC.2024.136819

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.45 No.2, pp.65 - 81

Received: 15 Mar 2023
Accepted: 10 May 2023
Published online: 22 Feb 2024 *

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Title: Consumer IoT device deployment optimisation through deep learning: a CNN-LSTM solution for traffic classification and service identification

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