Designing an Adaptive Intrusion Detection Model for IoT Smart Homes using Reinforcement Learning

Security threats are now a major concern for smart homes because of their expanding IoT device population. The security methods from traditional times fail to tackle the specific difficulties that arise with IoT ecosystems because of their limited resources and different devices and changing network patterns. A new IDS design built from deep learning components specifically for IoT homes exists as the foundation of this research. The proposed intrusion detection system uses a combination of CNN and LSTM networks to boost detection performance without generating large numbers of false alerts. A set of experimental results shows that the proposed intrusion detection system delivers superior outcomes than existing methods by achieving high accuracy together with quick responses and precise results. The research confirms how deep learning security solutions protect the IoT environments found in smart homes.