September 28, 2025Open Access

An AI-Enabled Federated Deep Learning Cybersecurity Framework for Decentralized Iot Ecosystems: A Privacy-Preserving Approach to Real-Time Threat Detection and Adaptive Defence

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Federated deep learning enhances real-time threat detection in IoT systems, indicating improved data privacy and device security.

The model achieved a high detection accuracy of 96.2% for cyber threats, emphasizing its effectiveness in safeguarding IoT devices.
Average latency for threat detection was recorded at 31.6 milliseconds, demonstrating responsiveness in real-time applications.
Utilizing both convolutional neural networks and long short-term memory networks, the system leverages advanced AI techniques to enhance security.
The findings support the use of federated learning architectures, suggesting potential for adaptive defenses against evolving cyber threats.

IGHOFIOMONI et al. (2025) studied this question.

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September 28, 2025Open Access

View Full Paper

Ask AI

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Authors

Federated deep learning enhances real-time threat detection in IoT systems, indicating improved data privacy and device security.

The model achieved a high detection accuracy of 96.2% for cyber threats, emphasizing its effectiveness in safeguarding IoT devices.
Average latency for threat detection was recorded at 31.6 milliseconds, demonstrating responsiveness in real-time applications.
Utilizing both convolutional neural networks and long short-term memory networks, the system leverages advanced AI techniques to enhance security.
The findings support the use of federated learning architectures, suggesting potential for adaptive defenses against evolving cyber threats.

IGHOFIOMONI et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark