A Hybrid Framework for the Sensitivity Analysis of Software-Defined Networking Performance Metrics Using Design of Experiments and Machine Learning Techniques

Software-defined networking (SDN) is a transformative approach for managing modern network architectures, particularly in Internet-of-Things (IoT) applications. However, ensuring the optimal SDN performance and security often needs a robust sensitivity analysis (SA). To complement existing SA methods, this study proposes a new SA framework that integrates design of experiments (DOE) and machine-learning (ML) techniques. Although existing SA methods have been shown to be effective and scalable, most of these methods have yet to hybridize anomaly detection and classification (ADC) and data augmentation into a single, unified framework. To fill this gap, a targeted application of well-established existing techniques is proposed. This is achieved by hybridizing these existing techniques to undertake a more robust SA of a typified SDN-reliant IoT network. The proposed hybrid framework combines Latin hypercube sampling (LHS)-based DOE and generative adversarial network (GAN)-driven data augmentation to improve SA and support ADC in SDN-reliant IoT networks. Hence, it is called DOE-GAN-SA. In DOE-GAN-SA, LHS is used to ensure uniform parameter sampling, while GAN is used to generate synthetic data to augment data derived from typified real-world SDN-reliant IoT network scenarios. DOE-GAN-SA also employs a classification and regression tree (CART) to validate the GAN-generated synthetic dataset. Through the proposed framework, ADC is implemented, and an artificial neural network (ANN)-driven SA on an SDN-reliant IoT network is carried out. The performance of the SDN-reliant IoT network is analyzed under two conditions: namely, a normal operating scenario and a distributed-denial-of-service (DDoS) flooding attack scenario, using throughput, jitter, and response time as performance metrics. To statistically validate the experimental findings, hypothesis tests are conducted to confirm the significance of all the inferences. The results demonstrate that integrating LHS and GAN significantly enhances SA, enabling the identification of critical SDN parameters affecting the modeled SDN-reliant IoT network performance. Additionally, ADC is also better supported, achieving higher DDoS flooding attack detection accuracy through the incorporation of synthetic network observations that emulate real-time traffic. Overall, this work highlights the potential of hybridizing LHS-based DOE, GAN-driven data augmentation, and ANN-assisted SA for robust network behavioral analysis and characterization in a new hybrid framework.