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…
CLEVER’s middleware enables AI applications to be deployed and managed efficiently across heterogeneous edge and cloud environments.🌟 Explore more: www.cleverproject.eu
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…
By combining photonics and AI, CLEVER ensures energy-efficient, secure, and scalable 6G networks for the future. 🌟 Explore more: www.cleverproject.eu
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…
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…
CLEVER Project delivered cutting-edge research in high-performance network scanning, hardware acceleration, and AI-driven threat detection: Network Scanning & Performance Hardware & Theoretical Optimization AI-Powered Security for 6G 🔗 Explore the technical details:
Why rely on centralized security when threats emerge at the edge? Our 6G base station-integrated AI model processes traffic flows locally, enabling real-time threat detection with minimal latency. Discover how distributed AI is reshaping network security. 🌟 Follow us on…
Supervised models fail when faced with new, unseen attacks. Our research leverages autoencoders + GMM clustering to detect anomalies without labeled data, achieving 92.2% F1-score on previously unknown DoS Goldeneye attacks. A critical step toward self-healing 6G networks. 🌟 Follow…
Imagine a network that detects, analyzes, and mitigates threats—autonomously. Our research brings this closer to reality with AI-driven base stations that learn from traffic patterns, adapt to new attacks, and minimize human intervention. 🌟 Follow us on LinkedIn! Check…
