Category News

The Cost of Downtime: Why 6G Needs Proactive Security

A single minute of service disruption can cost $5,600. Our AI-powered threat detection—embedded in 6G base stations—minimizes risks by identifying attacks as they emerge, not after the damage is done. Proactive security for a connected future. 🌟 Follow us on…

From 5G to 6G: Why Autonomous Security is Non-Negotiable

5G’s limitations in latency, global coverage, and security demand a 6G revolution. Our AI-driven threat detection—deployed at the base station—enables self-protecting networks that adapt to new attacks without human intervention. The future of telecom security starts here. 🌟 Follow us…

Real-Time vs. Offline: Why 6G Can’t Afford to Wait

Offline traffic analysis is too slow for 6G’s ultra-low latency requirements. Our real-time deep learning model processes packets on-the-fly, ensuring threats are mitigated before they disrupt services. Speed meets intelligence in next-gen networks. 🌟 Follow us on LinkedIn! Check…

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…

🌍 Sustainable 6G Networks

By combining photonics and AI, CLEVER ensures energy-efficient, secure, and scalable 6G networks for the future. 🌟 Explore more: www.cleverproject.eu

The Role of Autoencoders in 6G Threat Detection

Autoencoders aren’t just for compression—they learn latent representations of normal traffic to spot anomalies. Combined with Gaussian Mixture Models, our system detects threats without prior attack samples, making it ideal for zero-day exploit mitigation in 6G. 🌟 Follow us on…

DoS Hulk vs. Goldeneye: How AI Detects Them Differently

Not all DoS attacks are the same. Hulk exploits unique request patterns, while Goldeneye abuses HTTP Keep-Alive + NoCache. Our deep embedding clustering approach adapts to both, achieving 97.3% and 92.2% F1-scores, respectively. See how AI-driven feature learning outperforms traditional…