Network Security Architecture for Large-Scale IoT Infrastructure
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The rapid proliferation of Internet of Things (IoT) devices has transformed modern infrastructure, enabling unprecedented connectivity and data exchange across var ious sectors, including healthcare, manufacturing, and smart cities. However, this massive scale introduces significant security challenges, as traditional network architectures strug gle to accommodate the heterogeneous and resource-constrained nature of IoT devices. This chapter presents a comprehensive network security architecture designed specifically for large-scale IoT infrastructure. By integrating Software-Defined Networking (SDN) and Zero-Trust Architecture (ZTA) principles, the proposed framework ensures dynamic policy enforcement, continuous authentication, and robust threat mitigation. We evalu ate the architecture using the widely recognized CICIoT2023 dataset, demonstrating its efficacy in detecting and neutralizing common IoT threats such as botnets, distributed denial-of-service (DDoS) attacks, spoofing, and Man-in-The-Middle (MITM) attacks. The extensive simulation results highlight a scalable approach to securing IoT networks with out compromising performance, ensuring low latency and high detection accuracy even under substantial device loads.
References
- Ahmed Dawoud, Seyed Shahristani, and Chun Raun. “Deep learning and software defined networks: Towards secure IoT architecture”. In: Internet of Things 3 (2018), pp. 82–89.
- Claudio Zanasi, Silvio Russo, and Michele Colajanni. “Flexible zero trust architec ture for the cybersecurity of industrial IoT infrastructures”. In: Ad Hoc Networks 156 (2024), p. 103414.
- Euclides Carlos Pinto Neto et al. “CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment”. In: Sensors 23.13 (2023), p. 5941.
- Fabrizio Mangione, Claudio Savaglio, and Giancarlo Fortino. “Generative artificial intelligence for internet of things computing: A systematic survey”. In: ACM Com puting Surveys (2025).
- Manal Jazzaa Alanazi et al. “An adaptive hybrid cryptographic framework for resource-constrained IoT devices”. In: Electronics 14.23 (2025), p. 4666.
- Mohammed Ali Qasem et al. “Enhancement of cryptography algorithms for security of cloud-based IoT with machine learning models”. In: Scientific Reports (2026).
- Shiliang Zhang and Sabita Maharjan. “Cenergy3: An open software package for city energy 3d modeling”. In: arXiv preprint arXiv:2603.20361 (2026).
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