Sparse-selective quantization for real-time cyber threat detection in large-scale networks

We propose a sparse-selective quantization framework for real-time cyber threat detection in large-scale networks, which tackles the dual challenges of computational efficiency and detection accuracy. The proposed method unites sparsity-aware feature selection and dynamic precision quantization, which permits high-dimensional network traffic data to be analyzed in real time without losing critical attack signatures. The system architecture comprises three principal components: a sparsity-aware feature selector that detects uncommon yet distinguishing patterns, a dynamic precision quantizer which adjusts feature compression according to their importance, and a lightweight deep learning classifier employing a GRU-attention mechanism for effective threat classification. Furthermore, the framework interacts smoothly with traditional network monitoring tools and automated response systems, guaranteeing compatibility with current infrastructure. Our approach is distinguished by its capacity to harmonize accuracy and computational cost via mixed-precision quantization, accelerated by TensorRT to achieve inference latency below one millisecond. Experimental findings show notable advancements in both velocity and precision relative to conventional intrusion detection systems, especially for rare but severe threats. The proposed system is implemented with TensorFlow Lite, rendering it appropriate for edge deployment in environments with limited resources. This work advances the state-of-the-art in real-time cyber threat detection by unifying sparsity analysis, adaptive quantization, and efficient deep learning into a cohesive and scalable solution-distinct from prior work by linking feature-level sparsity patterns directly to dynamic quantization policies, rather than focusing on model activations or fixed parameters.