Enabling Secure Edge Intelligence: TinyML-Based Threat Detection in 5G and Future Networks

The evolution of 5G networks toward 6G presents an opportunity to shift from centralized security to decentralized, intelligent, and autonomous security. While 5G introduces decentralization through software-based virtualization and edge computing, it also exposes network infrastructure to a new and dynamic threat landscape that still relies largely on static, centrally deployed threat detection. This work proposes a lightweight anomaly detection framework based on Tiny Machine Learning (TinyML), specifically designed to address the latency, memory, and energy constraints of 5G edge devices. Using promising model compression techniques, including integer quantization and pruning, we demonstrate that highly optimized models can run entirely on-device without relying on cloud infrastructure. Our experiments using a real-world 5G dataset demonstrate that the quantized TinyDenseNet achieves over 99% detection accuracy while keeping the model size below 30 KB and inference latency under 11 ms. By embedding intelligent detection at the network edge, this approach presents self-defending, ultra-reliable, and context-aware infrastructures for future 6G networks. The proposed approach focuses on next-generation networks, in which decentralized, low-power, and privacy-preserving security mechanisms will be essential.