Experimental results show optical CNNs nearly match electronic models. Only 0.03% loss from full-precision baselines. 4-bit configurations balance performance and power perfectly. High accuracy meets hardware efficiency.🌟 Follow us on LinkedIn! Check the updates from the website: www.cleverproject.eu 🌟 Full paper in:…
Real-time DoS mitigation with sub-microsecond latency is now possible. Photonic accelerators process data at optical speed directly in the base station. 99.7% detection accuracy with minimal energy overhead. Cyber defense meets green performance.🌟 Follow us on LinkedIn! Check the updates from the…
AI-driven 6G base stations will autonomously detect network intrusions. This work deploys a Convolutional Neural Network on photonic hardware. Real-time DoS detection at the network edge ensures resilience and autonomy. Security evolves into a built-in neural reflex.🌟 Follow us on LinkedIn! Check…
The CLEVER Project is redefining AI-driven threat detection for 6G. From unsupervised learning to real-time mitigation, we’re building the autonomous networks of tomorrow. Explore our research and be part of the next telecom evolution. 🌟 Follow us on LinkedIn! …
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…
Our convolutional autoencoder + GMM model isn’t just theoretical—it’s tested on real-world datasets (CIC-IDS 2017) with 97%+ F1-scores. The next step? Deploying at scale in 6G networks. 🌟 Follow us on LinkedIn! Check the updates from the website: www.cleverproject.eu…
CLEVER Project advanced autonomous, AI-driven security for 6G networks, demonstrating how deep learning and unsupervised methods outperform traditional approaches: AI for DoS Detection & Mitigation Real-Time, Explainable Security Beyond Traditional Defenses 🔗 Explore the research: CLEVER Project: Full Paper:…
A supervised model trained on DoS Hulk saw its F1-score drop to 54.46% when tested on Goldeneye. Our unsupervised approach maintained 92%+ accuracy on both. The lesson? 6G needs adaptive, label-free AI. 🌟 Follow us on LinkedIn! Check the…
Supported by the EU’s Key Digital Technologies Joint Undertaking, our research introduces unsupervised deep learning for real-time 6G threat detection. Join us in shaping the future of autonomous, resilient networks. 🌟 Follow us on LinkedIn! Check the updates from…
How do you trust an AI’s threat detection? By visualizing its decisions. Using t-SNE, we mapped latent space clusters to show how our model distinguishes normal vs. malicious traffic. Transparency builds confidence in AI-driven 6G security. 🌟 Follow us on…
