End-to-End Performance Analysis of an Intelligent Zero-Trust Framework for SDN: A Quantitative Evaluation of Latency and Overhead

The integration of deep learning-based anomaly detection with zero-trust authentication in software-defined networking (SDN) improves security but introduces operational costs. This paper presents a comprehensive performance analysis of the Intelligent Zero-Trust Security Framework for SDN (IZTSDN). We develop an extended MiniIZTA testbed and measure the authentication latency, detection latency, mitigation latency, and resource usage across 4 to 64 nodes. Under normal conditions, the mean latency is 83±4 ms (95% CI, N=1000, σ=12 ms). Under DDoS attack, the mean latency increases to 235±11 ms (95% CI, N=1000, σ=38 ms), CPU usage reaches 94±2% (95% CI), and scalability becomes constrained beyond 16–32 nodes. The deep learning component is identified as the main bottleneck. We propose optimization strategies including hardware acceleration, efficient attention mechanisms, and a distributed architecture. These results provide practical guidance for deployment in virtualized, controller-centric SDN environments and offer a quantitative baseline for larger-scale extrapolation.