AI-Driven Quantum-Safe IoT Security Framework for Real-Time Threat Detection

Internet of Things (IoT) devices now run in factories, hospitals, buildings and other critical infrastructure, so the security of their communication matters more every year. Most deployments protect keys and identities with RSA or elliptic-curve cryptography (ECC), which a large fault-tolerant quantum computer could break. At the same time, IoT networks are exposed to everyday attacks such as denial of service, port scanning and spoofing. In practice, cryptographic protection and intrusion detection are built as two unrelated pieces. This paper proposes a framework that studies them together. It pairs the NIST post-quantum standards ML-KEM (key establishment) and ML-DSA (digital signatures) with a Random Forest classifier and a Long Short-Term Memory (LSTM) network for traffic analysis. An ESP32-class edge device, MQTT messaging, a signed event log and a monitoring dashboard complete the design. We describe the problem, the research gap, the architecture and the evaluation plan. The prototype is still to be built, so no measured results are reported.