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January 14, 2026Transactions on Emerging Telecommunications Technologies

Enabling A Better Learning Algorithm Compared With Machine Learning and Deep Learning Algorithms for Enhancing Security and Privacy in the Internet of Things Network

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Authors

AAAbdullah Saleh Alqahtani

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Overview

Compares machine learning and deep learning algorithms to improve security and privacy in IoT networks, indicating a more effective approach.

Key Points

  • The aim is to identify the most effective algorithm for securing IoT data while preserving privacy.
  • Development of an Intrusion Detection System using machine learning and deep learning algorithms
  • Implementation and testing of algorithms like Random Forest, CNNs, and DNNs
  • Comparison with traditional machine learning algorithms such as Decision Trees and XG-Boost
  • Utilization of the benchmark KDD dataset for analysis
  • Machine and deep learning algorithms demonstrate effective detection of intrusions and attacks in IoT networks
  • Performance metrics including accuracy, precision, recall, and F1 score indicate superior results for deep learning
  • The comparison reveals specific algorithms outperform others in securing IoT data

Cite This Study

Abdullah Saleh Alqahtani (2026) studied this question.

synapsesocial.com/papers/696719a7c0d1e3cfbfce9021https://doi.org/10.1002/ett.70341
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