ArticleScientific reports2025
Enhanced security for IoT cloud environments using EfficientNet and enhanced football team training algorithm.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A two-stage generative-AI fusion intrusion detection system for calibrated and reliable cloud security.Scientific reports · 2026Article
- Evaluation of building structural reinforcement performance by internet of things and UAV sensing.Scientific reports · 2026Article
- APVCPC: An Adaptive Predicted Value Computation and Pixel Classification Framework for Reversible Data Hiding in Encrypted Images.Sensors (Basel, Switzerland) · 2026Article
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3 authors.
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Abstract
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.
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