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Deep Learning-Based Intrusion Detection Framework for Securing IoT-Enabled Smart Homes

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This study reviews deep learning intrusion detection systems for IoT environments, suggesting improvements in security measures.

Automated deep learning methods significantly enhance intrusion detection accuracy in smart homes, addressing vulnerabilities.
The study categorizes existing intrusion detection frameworks into centralized, distributed, and hybrid architectures.
Common challenges in IoT include data scarcity and limited processing power, complicating real-time attacks detection.
Various deep learning models, like CNNs and GANs, are applicable to bolster security in IoT ecosystems.

Sangeetha et al. (2025) studied this question.

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View Full Paper

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Authors

This study reviews deep learning intrusion detection systems for IoT environments, suggesting improvements in security measures.

Automated deep learning methods significantly enhance intrusion detection accuracy in smart homes, addressing vulnerabilities.
The study categorizes existing intrusion detection frameworks into centralized, distributed, and hybrid architectures.
Common challenges in IoT include data scarcity and limited processing power, complicating real-time attacks detection.
Various deep learning models, like CNNs and GANs, are applicable to bolster security in IoT ecosystems.

Sangeetha et al. (2025) studied this question.

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