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<p>The growing implementation of Internet of Things (IoT) technology has resulted in a significant increase in the number of connected devices, thereby exposing IoT-cloud environments to a range of cyber threats. As the number of IoT devices continues to grow, the potential attack surface also enlarges, complicating the task of securing these systems. This paper introduces an innovative approach to intrusion detection that integrates EfficientNet with a newly refined metaheuristic known as the Enhanced Football Team Training Algorithm (EFTTA). The proposed EfficientNet/EFTTA model aims to identify anomalies and intrusions in IoT-cloud environments with enhanced accuracy and efficiency. The effectiveness of this model is measured using a standard dataset and is compared against some other methods during performance metrics. The results indicate that the proposed method surpasses existing techniques, demonstrating improved accuracy over 98.56% for NSL-KDD and 99.1% for BoT-IoT in controlled experiments for the protection of IoT-cloud infrastructures.</p>

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Enhanced security for IoT cloud environments using EfficientNet and enhanced football team training algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Jian Cui,
  • Lan Shi,
  • Ahmed Alkhayyat

摘要

The growing implementation of Internet of Things (IoT) technology has resulted in a significant increase in the number of connected devices, thereby exposing IoT-cloud environments to a range of cyber threats. As the number of IoT devices continues to grow, the potential attack surface also enlarges, complicating the task of securing these systems. This paper introduces an innovative approach to intrusion detection that integrates EfficientNet with a newly refined metaheuristic known as the Enhanced Football Team Training Algorithm (EFTTA). The proposed EfficientNet/EFTTA model aims to identify anomalies and intrusions in IoT-cloud environments with enhanced accuracy and efficiency. The effectiveness of this model is measured using a standard dataset and is compared against some other methods during performance metrics. The results indicate that the proposed method surpasses existing techniques, demonstrating improved accuracy over 98.56% for NSL-KDD and 99.1% for BoT-IoT in controlled experiments for the protection of IoT-cloud infrastructures.