Deep Learning-Based Autonomous Anomaly Detection for Security in SDN-IoT Networks

Converging Software-Defined Networking (SDN) and the Internet of Things (IoT) has directed innovative network architectures and applications. However, this fusion exposes security vulnerabilities due to SDN-IoT networks’ expanded attack surface and dynamic nature. Autonomous Anomaly Detection (AAD) plays a pivotal role in swiftly identifying and mitigating real-time security threats in this landscape. This survey thoroughly investigates AAD approaches within SDN-IoT networks, with a particular focus on applying advanced Deep Learning (DL) techniques. It explores the significance of SDN-IoT architecture, articulates the motivations driving AAD, and highlights its critical role in ensuring the availability, confidentiality, authentication, integrity, and authorization of networked resources. Furthermore, it defines the threat vectors across different layers of SDN-IoT, facilitating a comprehensive validation of existing attack and defence approaches. The paper provides an extensive review of DL-based AAD methods in SDN-IoT networks, examining their strengths, limitations, and practical implications. Notable contributions include an in-depth taxonomy of DL models—such as CNNs, RNNs, LSTMs, and autoencoders—designed to detect and mitigate various security threats, including DDoS attacks, data breaches, and abnormal traffic patterns. The performance of these techniques is evaluated using measures like accuracy, recall, and F1-score, with some methods achieving detection accuracy exceeding 99%. Additionally, this survey identifies key challenges in implementing AAD systems, such as scalability, real-time processing, and integration within resource-constrained IoT environments, while proposing future research directions to address these issues. In conclusion, this comprehensive survey summarizes the multifaceted landscape of DL-based AAD for Security in SDN-IoT Networks, providing a foundation for further exploration and innovation that can advance security within the evolving realm of interconnected networks.