03110nam a22001817a 4500 NUST 20260127102321.0 005.1,ZAB Zabeehullah 132928 Deep Learning Applications in SDN-Enabled Internet of Things / Zabeehullah Rawalpindi, MCS (NUST), 2025 xx, 167 p The rapid expansion of the Internet of Things (IoT) has led to an unprecedented surge in the number of connected devices, generating vast amounts of imbalance data and demanding sophisticated management solutions. Traditional network architectures struggle to cope with the dynamic and heterogeneous nature of IoT environments. Software Defined Networking (SDN) emerges as a powerful paradigm to address these challenges by decoupling the control plane from the data plane. Deep Learning (DL), a subset of Machine Learning (ML), has demonstrated remarkable capabilities in processing and analyzing large-scale data, positioning it as a powerful tool for improving SDN-IoT systems. Researchers have made significant efforts to address SDN-IoT challenges by applying ML and DL techniques. This thesis investigates the synergistic integration of DL techniques within SDN-IoT frameworks. The primary objective is to develop innovative DL-driven solutions that optimize network management, enhance security, and improve overall system performance in IoT ecosystems. The first proposed method employs a Generative Adversarial Network (GAN) model to accurately detect and classify minor class attacks, achieving impressive accuracy and F1-score values of 94.44% and 92.55%, respectively. The second technique utilizes a Deep Reinforcement Learning (DRL) model in the SDN-IoT environment to achieve secure routing optimization. It is evaluated using three benchmark metrics: throughput, latency, and the probability of avoiding malicious nodes. This method demonstrated superior throughput and latency performance compared to existing approaches in the literature. This thesis makes several key contributions to the field of IoT networking. Firstly, it provides a comprehensive analysis of the potential of DL to address critical challenges in SDN-enabled IoT environments. Secondly, it introduces novel DL models tailored for specific IoT applications, showcasing their effectiveness through extensive evaluations. Thirdly, it highlights the importance of interdisciplinary approaches, combining advancements in networking and Artificial Intelligence (AI) to drive innovation in IoT systems. Future research directions include exploring the integration of other emerging technologies, such as Federated Learning and Blockchain, with DL and SDN-IoT frameworks. Additionally, investigating the scalability of DL models in large-scale IoT deployments and their adaptability to new and evolving network conditions remains a promising area for further exploration. PhD Computer Software Engineering Thesis 132801 PhD CSE Thesis 132802 Supervised by Dr. Fahim Arif 132700 ddc THE 615953 615953 0 0 ddc 0 0 MCS MCS THE 2026-01-27 0 005.1,ZAB MCSPhD CSE-25 2026-01-27 2026-01-27 THE Almirah No.68, Shelf No.6