2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 187-193, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Basil Xavier 1 * , Jaspher Willsie Kathrine 2 , Priyadharsini 3 , Gladwin Rufus 4 , R. Venkatesan 5
Doi: https://doi.org/10.54216/FPA.190214
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.
Internet of Things (IoT) , Intrusion Detection Systems (IDS) , Convolutional Neural Networks (CNN) , Long Short-Term Memory (LSTM) , Genetic Algorithms (GA) , Ensemble Learning , Federated Learning , Reinforcement Learning , Cybersecurity
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