AI-driven cybersecurity for IoT networks : Enhancing threat detection and mitigation
Mallellu Sai Prashanth, Rajanikanth Aluvalu, V. Uma Maheswari, Siripuri Kiran, Sathwik Narkedimilli, Palamakula Ramesh Babu · Journal of Information and Optimization Sciences · 2025
The proliferation of Internet of Things (IoT) devices has increased the size and complexity of cyber security threats to such an extent that traditional security is insufficient to safeguard IoT networks. This research uses Artificial Intelligence (AI) to provide enhanced real-time security through advanced anomaly detection features. The study considers various new methodologies, including federated learning for privacy-preserving but threat detection, graph neural networks (GNNs) to monitor attack propagation, and unsupervised anomaly detection through autoencoders and generative adversarial networks (GANs). In addition, research in zero-trust AI security architecture will also be performed to further secure IoT networks by verifying each access request. Integrating AI-driven approaches delivers an end-to-end, proactive solution for protecting IoT ecosystems against future cyber-attacks.