Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3670 2018 2018 Karunya Institute of Technology and Sciences , Coimbatore, India Basil Basil Karunya Institute of Technology and Sciences , Coimbatore, India Jaspher Willsie Kathrine Karunya Institute of Technology and Sciences , Coimbatore, India Priyadharsini Priyadharsini Karunya Institute of Technology and Sciences , Coimbatore, India Gladwin Rufus Karunya Institute of Technology and Sciences , Coimbatore, India R. Venkatesan In the context of dynamic and highly diverse IoT (Internet of Things), the nature of threats and the amount of data that needs to be processed by IDSs (Intrusion Detection System) have become much greater and represent considerable problems for modern security systems. This work presents a new method called a Hybrid Deep Learning-Evolutionary Algorithm with an Ensemble Strategy (HDLE-EASE) for improving intrusion detection in IoT systems. Our method combines the spatial feature extraction capability of CNN (Convolutional Neural Networks) and temporal feature extraction of LSTM (Long Short-Term Memory) networks with the optimization feature of GA to optimize model parameters. We further incorporate a composite of boosting-bagging hybrid to enhance the stability and reliability of the intrusion detection mechanism. As privacy and scalability are critical issues in IoT networks, we propose a federated learning approach, allowing for model training on IoT networks while preserving data privacy. Furthermore, the presented approach includes a reinforcement-learning module for the capability of adapting to newly emerge and changing security threats. Initial tests show that the detection accuracy and model optimization capabilities of HDLE-EASE significantly outperform other methods, while its adaptability makes the tool a promising one for developing a holistic solution to protect IoT systems against a wide range of attacks. 2025 2025 187 193 10.54216/FPA.190214 https://www.americaspg.com/articleinfo/3/show/3670