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This is a Quantitative Study study.

A Unified Adaptive Cyber Threat Intelligence Model for Real-Time IoT Security Using Machine Learning and GAN-Based Augmentation

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This model enhances security by detecting threats in real time using machine learning and generative methods.

The model achieves 92.86% accuracy in detecting IoT intrusions, highlighting its effectiveness in real-time scenarios.
Using a combination of DBSCAN and CNN-LSTM, the model identifies and categorizes evolving threats efficiently.
A significant feature is the use of a GAN to generate synthetic data, which improves detection of underrepresented classes.
The LDQN approach dynamically responds to threats by determining actions like BLOCK or DROP based on threat severity.

Mwende et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

This model enhances security by detecting threats in real time using machine learning and generative methods.

The model achieves 92.86% accuracy in detecting IoT intrusions, highlighting its effectiveness in real-time scenarios.
Using a combination of DBSCAN and CNN-LSTM, the model identifies and categorizes evolving threats efficiently.
A significant feature is the use of a GAN to generate synthetic data, which improves detection of underrepresented classes.
The LDQN approach dynamically responds to threats by determining actions like BLOCK or DROP based on threat severity.

Mwende et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark