Deep Learning Meets IoT: Hybrid Approaches for Botnet Detection

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Year : 2025 | Volume :12 | Issue : 01 | Page : 31-40
By
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Sumit Kumar Soni,

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Sreeja Nair,

  1. M Tech Scholar, Department of Computer Science, Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India
  2. HOD, Department of Computer Science, Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India

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Rapid advancement in the Internet of Things (IoT) changed everything, making it possible for seamless interconnectivity of devices and altering data-driven decision processes. This study delves into the intersection of IoT with deep learning approaches and hybrid approaches for managing botnet in IoT systems, especially security, efficiency, and performance optimization. Leveraging deep learning models, for example, CNNs and RNNs, will help the network achieve more intrusion detection and data analysis. Additionally, this paper has further discussed how edge and cloud computing can be used for increasing latency and reducing the operational cost and improving data privacy. It also compares different approaches of hybrid security techniques to show how these effectively reduce vulnerabilities in an IoT device. The findings emphasize the potential of integrating deep learning with IoT to develop robust, efficient, and secure systems.

Keywords: IoT, deep learning, edge computing, hybrid techniques, intrusion detection and data privacy

[This article belongs to Recent Trends in Electronics Communication Systems (rtecs)]

How to cite this article:
Sumit Kumar Soni, Sreeja Nair. Deep Learning Meets IoT: Hybrid Approaches for Botnet Detection. Recent Trends in Electronics Communication Systems. 2024; 12(01):31-40.
How to cite this URL:
Sumit Kumar Soni, Sreeja Nair. Deep Learning Meets IoT: Hybrid Approaches for Botnet Detection. Recent Trends in Electronics Communication Systems. 2024; 12(01):31-40. Available from: https://journals.stmjournals.com/rtecs/article=2024/view=0

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Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 02/12/2024
Accepted 09/12/2024
Published 24/12/2024
214