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Tag GMM

The Role of Expectation-Maximization in 6G Threat Detection

Gaussian Mixture Models rely on EM algorithms to cluster latent space embeddings. By iteratively refining means, covariances, and weights, our model achieves 96%+ precision in identifying anomalies. Math meets security in 6G. 🌟 Follow us on LinkedIn! Check the…

GMM Clustering: Turning Deep Embeddings into Actionable Insights

Gaussian Mixture Models (GMM) don’t just cluster—they provide probabilistic insights into traffic anomalies. By analyzing latent space embeddings from our autoencoder, we detect threats with 96.9% precision, even in imbalanced traffic scenarios. 🌟 Follow us on LinkedIn! Check the…