Toward a deep learning-based intrusion detection system for IoT against botnet attacks

The massive network traffic data between connected devices in the internet of things have taken a big challenge to many traditional intrusion detection systems (IDS) to find probable security breaches. However, security attacks lean towards unpredictability. There are numerous difficulties to build...

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Bibliographic Details
Published in:IAES International Journal of Artificial Intelligence Vol. 10; no. 1; p. 110
Main Authors: Idrissi, Idriss, Boukabous, Mohammed, Azizi, Mostafa, Moussaoui, Omar, El Fadili, Hakim
Format: Journal Article
Language:English
Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.03.2021
Subjects:
Artificial neural networks
Communications traffic
Deep learning
Electronic devices
Internet of Things
Intrusion detection systems
Malware
Security
Security management
ISSN:2089-4872, 2252-8938, 2089-4872
Online Access:Get full text
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Summary:The massive network traffic data between connected devices in the internet of things have taken a big challenge to many traditional intrusion detection systems (IDS) to find probable security breaches. However, security attacks lean towards unpredictability. There are numerous difficulties to build up adaptable and powerful IDS for IoT in order to avoid false alerts and ensure a high recognition precision against attacks, especially with the rising of Botnet attacks. These attacks can even make harmless devices becoming zombies that send malicious traffic and disturb the network. In this paper, we propose a new IDS solution, baptized BotIDS, based on deep learning convolutional neural networks (CNN). The main interest of this work is to design, implement and test our IDS against some well-known Botnet attacks using a specific Bot-IoT dataset. Compared to other deep learning techniques, such as simple RNN, LSTM and GRU, the obtained results of our BotIDS are promising with 99.94% in validation accuracy, 0.58% in validation loss, and the prediction execution time is less than 0.34 ms.
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ISSN:2089-4872
2252-8938
2089-4872
DOI:10.11591/ijai.v10.i1.pp110-120